Corrigendum: Edwardsiella piscicida infection reshapes the intestinal microbiome and metabolome of big-belly seahorses: mechanistic insights of synergistic actions of virulence factors.

[This corrects the article DOI: 10.3389/fimmu.2023.1135588.].

Edwardsiella piscicida infection reshapes the intestinal microbiome and metabolome of big-belly seahorses: mechanistic insights of synergistic actions of virulence factors.

Uncovering the mechanism underlying the pathogenesis of Edwardsiella piscicida-induced enteritis is essential for global aquaculture. In the present study, we identified E. piscicida as a lethal pathogen of the big-belly seahorse (Hippocampus abdominalis) and revealed its pathogenic pattern and characteristics by updating our established bacterial enteritis model and evaluation system. Conjoint analysis of metagenomic and metabolomic data showed that 15 core virulence factors could mutually coordinate the remodeling of intestinal microorganisms and host metabolism and induce enteritis in the big-belly seahorse. Specifically, the Flagella, Type IV pili, and Lap could significantly increase the activities of the representative functional pathways of both flagella assembly and bacterial chemotaxis in the intestinal microbiota (P < 0.01) to promote pathogen motility, adherence, and invasion. Legiobactin, IraAB, and Hpt could increase ABC transporter activity (P < 0.01) to compete for host nutrition and promote self-replication. Capsule1, HP-NAP, and FarAB could help the pathogen to avoid phagocytosis. Upon entering epithelial cells and phagocytes, Bsa T3SS and Dot/Icm could significantly increase bacterial secretion system activity (P < 0.01) to promote the intracellular survival and replication of the pathogen and the subsequent invasion of the neighboring tissues. Finally, LPS3 could significantly increase lipopolysaccharide biosynthesis (P < 0.01) to release toxins and kill the host. Throughout the pathogenic process, BopD, PhoP, and BfmRS significantly activated the two-component system (P < 0.01) to coordinate with other VFs to promote deep invasion. In addition, the levels of seven key metabolic biomarkers, Taurine, L-Proline, Uridine, L-Glutamate, Glutathione, Xanthosine, and L-Malic acid, significantly decreased (P < 0.01), and they can be used for characterizing E. piscicida infection. Overall, the present study systematically revealed how a combination of virulence factors mediate E. piscicida-induced enteritis in fish for the first time, providing a theoretical reference for preventing and controlling this disease in the aquaculture of seahorses and other fishes.

A BPNN-based ecologically extended input-output model for virtual water metabolism network management of Kazakhstan.

In this study, a back-propagation-neural-network-based ecologically extended input-output model (abbreviated as BPNN-EIOM) is developed for virtual water metabolism network (VWMN) management. BPNN-EIOM can identify key consumption sectors, simulate performance of VWMN, and predict water consumption. BPNN-EIOM is then applied to analyzing VWMN of Kazakhstan, where multiple scenarios under different gross domestic production (GDP) growth rates, sectoral added values, and final demands are designed for determining the optimal management strategies. The major findings are (i) Kazakhstan typically relies on net virtual water import (reaching 1497.9 × 106 m3 in 2015); (ii) agriculture is the major exporter and advanced manufacture is the major importer; (iii) by 2025, Kazakhstan's water consumption would increase to [19322, 22016] × 106 m3 under multiple scenarios; (iv) when Kazakhstan's GDP growth rate, manufacturing's added value, and final demand are scheduled to 5.5%, 8.5%, and 5.8%, its VWMN can reach the optimum. The findings are useful for decision makers to optimize Kazakhstan's industrial structure, mitigate the national water scarcity, and promote its socio-economic sustainable development.

Contact and Tribological Study of Micro/Nano Groove Texture on the Surface of Gas Bearing Materials Based on Nanoscale.

As a kind of sliding bearing, the gas bearing is widely used in high-speed rotating machinery. It realizes energy cleaning in the field of high-speed rotating machinery. In order to solve the problem of reducing the service life of gas bearings due to friction during startup and shutdown, we use micromachining technology to process groove textures with different groove widths on the surface of 0Cr17Ni7Al, a common material for gas bearings. A ball-disc friction contrast test is conducted under dry friction conditions with and without texture. The experiment shows that the lowest average friction coefficient of 0.8 mm texture is σ = 0.745. When the friction radius is 22.5 mm, the wear rate of 1.0 mm texture is the lowest at ω = 3.118 × 10-4mm3/N·mm. However, the maximum friction coefficient reached is σ = 0.898. Under the nanometer scale, the contact between friction pairs is fully analyzed. The influence mechanism of different groove widths, friction impacts and climbing heights on the friction and wear properties of the micromechanical groove texture on the surface of 0Cr17Ni7Al stainless steel is studied at the nano-fractal scale. The effects of different width grooves on the surface texture and tribological properties of the micromachine are studied.