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Sinorhizobium fredii CCBAU45436 is an excellent rhizobium that plays an important role in agricultural production. However, there still needs more comprehensive understanding of the metabolic system of S. fredii CCBAU45436, which hinders its application in agriculture. Therefore, based on the first-generation metabolic model iCC541 we developed a new genome-scale metabolic model iAQY970, which contains 970 genes, 1,052 reactions, 942 metabolites and is scored 89% in the MEMOTE test. Cell growth phenotype predicted by iAQY970 is 81.7% consistent with the experimental data. The results of mapping the proteome data under free-living and symbiosis conditions to the model showed that the biomass production rate in the logarithmic phase was faster than that in the stable phase, and the nitrogen fixation efficiency of rhizobia parasitized in cultivated soybean was higher than that in wild-type soybean, which was consistent with the actual situation. In the symbiotic condition, there are 184 genes that would affect growth, of which 94 are essential; In the free-living condition, there are 143 genes that influence growth, of which 78 are essential. Among them, 86 of the 94 essential genes in the symbiotic condition were consistent with the prediction of iCC541, and 44 essential genes were confirmed by literature information; meanwhile, 30 genes were identified by DEG and 33 genes were identified by Geptop. In addition, we extracted four key nitrogen fixation modules from the model and predicted that sulfite reductase (EC 1.8.7.1) and nitrogenase (EC 1.18.6.1) as the target enzymes to enhance nitrogen fixation by MOMA, which provided a potential focus for strain optimization. Through the comprehensive metabolic model, we can better understand the metabolic capabilities of S. fredii CCBAU45436 and make full use of it in the future.

Abstract Background The natural products, metabolites, of gut microbes are crucial effect factors on diseases. Comprehensive identification and annotation of relationships among disease, metabolites, and microbes can provide efficient and targeted solutions towards understanding the mechanism of complex disease and development of new markers and drugs. Results We developed Gut Microbial Metabolite Association with Disease (GMMAD), a manually curated database of associations among human diseases, gut microbes, and metabolites of gut microbes. Here, this initial release (i) contains 3,836 disease-microbe associations and 879,263 microbe-metabolite associations, which were extracted from literatures and available resources and then experienced our manual curation; (ii) defines an association strength score and a confidence score. With these two scores, GMMAD predicted 220,690 disease-metabolite associations, where the metabolites all belong to the gut microbes. We think that the positive effective (with both scores higher than suggested thresholds) associations will help identify disease marker and understand the pathogenic mechanism from the sense of gut microbes. The negative effective associations would be taken as biomarkers and have the potential as drug candidates. Literature proofs supported our proposal with experimental consistence; (iii) provides a user-friendly web interface that allows users to browse, search, and download information on associations among diseases, metabolites, and microbes. The resource is freely available at http://guolab.whu.edu.cn/GMMAD . Conclusions As the online-available unique resource for gut microbial metabolite-disease associations, GMMAD is helpful for researchers to explore mechanisms of disease- metabolite-microbe and screen the drug and marker candidates for different diseases.

Osteochondral injury is a common and frequent orthopedic disease that can lead to more serious degenerative joint disease. Tissue engineering is a promising modality for osteochondral repair, but the implanted scaffolds are often immunogenic and can induce unwanted foreign body reaction (FBR). Here, we prepare a polypept(o)ide-based PAA-RGD hydrogel using a novel thiol/thioester dual-functionalized hyperbranched polypeptide P(EG3Glu-co-Cys) and maleimide-functionalized polysarcosine under biologically benign conditions. The PAA-RGD hydrogel shows suitable biodegradability, excellent biocompatibility, and low immunogenicity, which together lead to optimal performance for osteochondral repair in New Zealand white rabbits even at the early stage of implantation. Further in vitro and in vivo mechanistic studies corroborate the immunomodulatory role of the PAA-RGD hydrogel, which induces minimum FBR responses and a high level of polarization of macrophages into the immunosuppressive M2 subtypes. These findings demonstrate the promising potential of the PAA-RGD hydrogel for osteochondral regeneration and highlight the importance of immunomodulation. The results may inspire the development of PAA-based materials for not only osteochondral defect repair but also various other tissue engineering and bio-implantation applications.

Coal water slurry is an advanced and efficient coal utilization technology, prepare coal water slurry by gasification wastewater can realize the recycling of wastewater and improve the combustion or gasification efficiency of the slurry. Stabilizer is an important component to ensure fluidity and stability in industrial applications of coal water slurry, and the complex substances in wastewater for coal water slurry preparation have a great influence on the stabilizer. Therefore, in this paper, the influence of the major substances in gasification wastewater on the stabilizer is studied. Results show that (a) Ammonia nitrogen substances have adverse effects on stabilizers, because the ionized NH4+ in the solution will bond with the polar groups on the stabilizer, which will affect the stabilizer to lose its effectiveness and reduce the stability of the slurry. (b) Small organic molecules have little effect on stabilizers, this is because the binding strength and quantity of stabilizer to water molecules is much larger than that of organic matters such as phenolic substances. (c) Highvalent metal ions have a greater impact on stabilizers, the metal ions easily form gels or precipitates with the stabilizer, causing it to lose its effectiveness, and also have an electrostatic effect with the carboxyl group on the side chain of the stabilizer, which makes the stabilizer easy to aggregate together and lose the steric hindrance effect, finally cause the stability of the slurry reduced. The conclusion of the influence of substances in wastewater on stabilizer can provide a technical reference for the selection of suitable wastewater and stabilizer to prepare coal water slurry, then promoting the efficient use of energy.

Synthetic lethality (SL) occurs when mutations in two genes together lead to cell or organism death, while a single mutation in either gene does not have a significant impact. This concept can also be extended to three or more genes for SL. Computational and experimental methods have been developed to predict and verify SL gene pairs, especially for yeast and Escherichia coli. However, there is currently a lack of a specialized platform to collect microbial SL gene pairs. Therefore, we designed a synthetic interaction database for microbial genetics that collects 13,313 SL and 2,994 Synthetic Rescue (SR) gene pairs that are reported in the literature, as well as 86,981 putative SL pairs got through homologous transfer method in 281 bacterial genomes. Our database website provides multiple functions such as search, browse, visualization, and Blast. Based on the SL interaction data in the S. cerevisiae, we review the issue of duplications' essentiality and observed that the duplicated genes and singletons have a similar ratio of being essential when we consider both individual and SL. The Microbial Synthetic Lethal and Rescue Database (Mslar) is expected to be a useful reference resource for researchers interested in the SL and SR genes of microorganisms. Mslar is open freely to everyone and available on the web at http://guolab.whu.edu.cn/Mslar/.

The beneficial metabolites of the microbiome could be used as a tool for screening drugs that have the potential for the therapy of various human diseases. Narrowing down the range of beneficial metabolite candidates in specific diseases was primarily a key step for further validation in model organisms. Herein, we proposed a reasonable hypothesis that the metabolites existing commonly in multiple beneficial (or negatively associated) bacteria might have a high probability of being effective drug candidates for specific diseases. According to this hypothesis, we screened metabolites associated with seven human diseases. For type I diabetes, 45 out of 88 screened metabolites had been reported as potential drugs in the literature. Meanwhile, 18 of these metabolites were specific to type I diabetes. Additionally, metabolite correlation could reflect disease relationships in some sense. Our results have demonstrated the potential of bioinformatics mining gut microbes' metabolites as drug candidates based on reported numerous microbe-disease associations and the Virtual Metabolic Human database. More subtle methods would be developed to ensure more accurate predictions.