JBIR-34 and -35 produced by Streptomyces sp. Sp080513GE-23 are nonribosomal peptides that possess an unusual 4-methyloxazoline moiety. Through draft genome sequencing, cosmid cloning, and gene disruption, the JBIR-34 and -35 biosynthesis gene cluster (fmo cluster) was identified; it encodes 20 proteins including five nonribosomal peptide synthetases (NRPSs). Disruption of one of these NRPS genes (fmoA3) resulted in no JBIR-34 and -35 production and accumulation of 6-chloro-4-hydroxyindole-3-carboxylic acid. Stable isotope-feeding experiments indicated that the methyl group of the methyloxazoline ring is derived from alanine rather than methionine. A recombinant FmoH protein, a glycine/serine hydroxymethyltransferase homolog, catalyzed conversion of α-methyl-l-serine into d-alanine (the reverse reaction of α-methyl-l-serine synthesis catalyzed by FmoH in vivo). Taken together, we concluded that α-methyl-l-serine synthesized from d-alanine is incorporated into JBIR-34 and -35 to form the 4-methyloxazoline moiety. We also propose the biosynthesis pathway of JBIR-34 and -35.

BACKGROUND Human health status can be measured in several different ways and statistical relationships among various measurements can be represented as a joint probability distribution. Approximation of the current health status of individuals will allow for more personalized and preventive healthcare by informing the potential risks and developing personalized interventions. Understanding the modifiable risk factors related to lifestyle, diet, and physical activity will facilitate the design of optimal treatment approaches for individuals. OBJECTIVE This study aims to provide a high-dimensional, cross-sectional dataset of comprehensive healthcare information to construct a virtual human generative model (VHGM) based on a joint probability distribution. METHODS In this cross-sectional observational study, data will be collected from a population of 1000 adult men and women (aged ≥20 years) matching the age ratio of the typical adult Japanese population. Data will include biochemical and metabolic profiles from blood, urine, saliva, and oral glucose tolerance tests; bacterial profiles from feces, facial skin, scalp skin, and saliva; mRNA, proteome, and metabolite analyses from facial and scalp skin surface lipids; lifestyle survey and questionnaire; physical, motor, cognitive, and vascular function analyses; alopecia; and comprehensive analyses of body odor components. Statistical analyses will examine multiple health-related items using a joint probability distribution model. We will train a joint probability distribution, the VHGM, by combining a commercially available healthcare dataset containing large amounts of relatively low-dimensional data with a high-dimensional, cross-sectional dataset. The trained VHGM is expected to enable various healthcare applications through application program interface calls. RESULTS Written informed consent will be required to participate in the study. The study has been approved by the Institutional Review Boards of the Kao Corporation (Approval # K0023-2108) and the Preferred Network, Inc. (Approval # ET22110047). CONCLUSIONS The collected data are expected to provide information on the relationships between various health statuses. Because different degrees of health status correlations are expected to have different effects on individual health status, this study will contribute to developing empirically justified interventions based on the population. CLINICALTRIAL The trial is registered with the University Hospital Medical Information Network (Registration No. UMIN000045746).

Background Human health status can be measured on the basis of many different parameters. Statistical relationships among these different health parameters will enable several possible health care applications and an approximation of the current health status of individuals, which will allow for more personalized and preventive health care by informing the potential risks and developing personalized interventions. Furthermore, a better understanding of the modifiable risk factors related to lifestyle, diet, and physical activity will facilitate the design of optimal treatment approaches for individuals. Objective This study aims to provide a high-dimensional, cross-sectional data set of comprehensive health care information to construct a combined statistical model as a single joint probability distribution and enable further studies on individual relationships among the multidimensional data obtained. Methods In this cross-sectional observational study, data were collected from a population of 1000 adult men and women (aged ≥20 years) matching the age ratio of the typical adult Japanese population. Data include biochemical and metabolic profiles from blood, urine, saliva, and oral glucose tolerance tests; bacterial profiles from feces, facial skin, scalp skin, and saliva; messenger RNA, proteome, and metabolite analyses of facial and scalp skin surface lipids; lifestyle surveys and questionnaires; physical, motor, cognitive, and vascular function analyses; alopecia analysis; and comprehensive analyses of body odor components. Statistical analyses will be performed in 2 modes: one to train a joint probability distribution by combining a commercially available health care data set containing large amounts of relatively low-dimensional data with the cross-sectional data set described in this paper and another to individually investigate the relationships among the variables obtained in this study. Results Recruitment for this study started in October 2021 and ended in February 2022, with a total of 997 participants enrolled. The collected data will be used to build a joint probability distribution called a Virtual Human Generative Model. Both the model and the collected data are expected to provide information on the relationships between various health statuses. Conclusions As different degrees of health status correlations are expected to differentially affect individual health status, this study will contribute to the development of empirically justified interventions based on the population. International Registered Report Identifier (IRRID) DERR1-10.2196/47024