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Dietary factors such as food texture affect feeding behavior and energy metabolism, potentially causing obesity and type 2 diabetes. We previously found that rats fed soft pellets (SPs) were neither hyperphagic nor overweight but demonstrated glucose intolerance, insulin resistance, and hyperplasia of pancreatic β-cells. In the present study, we investigated the mechanism of muscle atrophy in rats that had been fed SPs on a 3-h time-restricted feeding schedule for 24 weeks. As expected, the SP rats were normal weight; however, they developed insulin resistance, glucose intolerance, and fat accumulation. In addition, skeletal muscles of SP rats were histologically atrophic and demonstrated disrupted insulin signaling. Furthermore, we learned that the muscle atrophy of the SP rats developed via the IL-6-STAT3-SOCS3 and ubiquitin-proteasome pathways. Our data show that the dietary habit of consuming soft foods can lead to not only glucose intolerance or insulin resistance but also muscle atrophy., (© 2024. The Author(s).)

Functional diversity rather than species richness is critical for the understanding of ecological patterns and processes. This study aimed to develop novel integrated analytical strategies for the functional characterization of fish diversity based on the quantification, prediction and integration of the chemical and physical features in fish muscles. Machine learning models with an improved random forest algorithm applied on 1867 muscle nuclear magnetic resonance spectra belonging to 249 fish species successfully predicted the mobility patterns of fishes into four categories (migratory, territorial, rockfish, and demersal) with accuracies of 90.3-95.4%. Markov blanket-based feature selection method with an ecological-chemical-physical integrated network based on the Bayesian network inference algorithm highlighted the importance of nitrogen metabolism, which is critical for environmental adaptability of fishes in nutrient-rich environments, in the functional characterization of fish biodiversity. Our study provides valuable information and analytical strategies for fish home-range assessment on the basis of the chemical and physical characterization of fish muscle, which can serve as an ecological indicator for fish ecotyping and human impact monitoring.

Runx2 and Sp7 are essential transcription factors for osteoblast differentiation. However, the molecular mechanisms responsible for the proliferation of osteoblast progenitors remain unclear. The early onset of Runx2 expression caused limb defects through the Fgfr1-3 regulation by Runx2. To investigate the physiological role of Runx2 in the regulation of Fgfr1-3, we compared osteoblast progenitors in Sp7 -/- and Runx2 -/- mice. Osteoblast progenitors accumulated and actively proliferated in calvariae and mandibles of Sp7 -/- but not of Runx2 -/- mice, and the number of osteoblast progenitors and their proliferation were dependent on the gene dosage of Runx2 in Sp7 -/- background. The expression of Fgfr2 and Fgfr3, which were responsible for the proliferation of osteoblast progenitors, was severely reduced in Runx2 -/- but not in Sp7 -/- calvariae. Runx2 directly regulated Fgfr2 and Fgfr3, increased the proliferation of osteoblast progenitors, and augmented the FGF2-induced proliferation. The proliferation of Sp7 -/- osteoblast progenitors was enhanced and strongly augmented by FGF2, and Runx2 knockdown reduced the FGF2-induced proliferation. Fgfr inhibitor AZD4547 abrogated all of the enhanced proliferation. These results indicate that Runx2 is required for the proliferation of osteoblast progenitors and induces proliferation, at least partly, by regulating Fgfr2 and Fgfr3 expression.

Data-driven approaches were applied to investigate the temporal and spatial changes of 1,022 individuals of wild yellowfin goby and its potential interaction with the estuarine environment in Japan. Nuclear magnetic resonance (NMR)-based metabolomics revealed that growth stage is a primary factor affecting muscle metabolism. Then, the metabolic, elemental and microbial profiles of the pooled samples generated according to either the same habitat or sampling season as well as the river water and sediment samples from their habitats were measured using NMR spectra, inductively coupled plasma optical emission spectrometry and next-generation 16 S rRNA gene sequencing. Hidden interactions in the integrated datasets such as the potential role of intestinal bacteria in the control of spawning migration, essential amino acids and fatty acids synthesis in wild yellowfin goby were further extracted using correlation clustering and market basket analysis-generated networks. Importantly, our systematic analysis of both the seasonal and latitudinal variations in metabolome, ionome and microbiome of wild yellowfin goby pointed out that the environmental factors such as the temperature play important roles in regulating the body homeostasis of wild fish.

Computer-based technological innovation provides advancements in sophisticated and diverse analytical instruments, enabling massive amounts of data collection with relative ease. This is accompanied by a fast-growing demand for technological progress in data mining methods for analysis of big data derived from chemical and biological systems. From this perspective, use of a general "linear" multivariate analysis alone limits interpretations due to "non-linear" variations in metabolic data from living organisms. Here we describe a kernel principal component analysis (KPCA)-incorporated analytical approach for extracting useful information from metabolic profiling data. To overcome the limitation of important variable (metabolite) determinations, we incorporated a random forest conditional variable importance measure into our KPCA-based analytical approach to demonstrate the relative importance of metabolites. Using a market basket analysis, hippurate, the most important variable detected in the importance measure, was associated with high levels of some vitamins and minerals present in foods eaten the previous day, suggesting a relationship between increased hippurate and intake of a wide variety of vegetables and fruits. Therefore, the KPCA-incorporated analytical approach described herein enabled us to capture input-output responses, and should be useful not only for metabolic profiling but also for profiling in other areas of biological and environmental systems.

Effective use of agricultural residual biomass may be beneficial for both local and global ecosystems. Recently, biochar has received attention as a soil enhancer, and its effects on plant growth and soil microbiota have been investigated. However, there is little information on how the physical, chemical, and biological properties of soil amended with biochar are affected. In this study, we evaluated the effects of the incorporation of torrefied plant biomass on physical and structural properties, elemental profiles, initial plant growth, and metabolic and microbial dynamics in aridisol from Botswana. Hemicellulose in the biomass was degraded while cellulose and lignin were not, owing to the relatively low-temperature treatment in the torrefaction preparation. Water retentivity and mineral availability for plants were improved in soils with torrefied biomass. Furthermore, fertilization with 3% and 5% of torrefied biomass enhanced initial plant growth and elemental uptake. Although the metabolic and microbial dynamics of the control soil were dominantly associated with a C1 metabolism, those of the 3% and 5% torrefied biomass soils were dominantly associated with an organic acid metabolism. Torrefied biomass was shown to be an effective soil amendment by enhancing water retentivity, structural stability, and plant growth and controlling soil metabolites and microbiota.

Environmental metabolomics or ionomics is widely used to characterize the effects of environmental stressors on the health of aquatic organisms. However, most studies have focused on liver and muscle tissues of fish, and little is known about how the other organs are affected by environmental perturbations and effects such as metal pollutants or eutrophication. We examined the metabolic and mineral profiles of three kinds of abundant fishes in estuarine ecosystem, yellowfin goby, urohaze-goby, and juvenile Japanese seabass sampled from Tsurumi River estuary, Japan. Multivariate analyses, including nuclear magnetic resonance-based metabolomics and inductively coupled plasma optical emission spectrometry-based ionomics approaches, revealed that the profiles were clustered according to differences among body tissues rather than differences in body size, sex, and species. The metabolic and mineral profiles of the muscle and fin tissues, respectively, suggest that these tissues are most appropriate for evaluating environmental perturbations. Such analyses will be highly useful in evaluating the environmental variation and diversity in aquatic ecosystems.