A system-level framework of complex microbe–microbe and host–microbe chemical cross-talk would help elucidate the role of our gut microbiota in health and disease. Here we report a literature-curated interspecies network of the human gut microbiota, called NJS16. This is an extensive data resource composed of ∼570 microbial species and 3 human cell types metabolically interacting through >4,400 small-molecule transport and macromolecule degradation events. Based on the contents of our network, we develop a mathematical approach to elucidate representative microbial and metabolic features of the gut microbial community in a given population, such as a disease cohort. Applying this strategy to microbiome data from type 2 diabetes patients reveals a context-specific infrastructure of the gut microbial ecosystem, core microbial entities with large metabolic influence, and frequently produced metabolic compounds that might indicate relevant community metabolic processes. Our network presents a foundation towards integrative investigations of community-scale microbial activities within the human gut.
A facile method has been developed to detect pathogenic bacteria using magnetic nanoparticle clusters (MNCs) and a 3D-printed helical microchannel. Antibody-functionalized MNCs were used to capture E. coli (EC) bacteria in milk, and the free MNCs and MNC-EC complexes were separated from the milk using a permanent magnet. The free MNCs and MNC-EC complexes were dispersed in a buffer solution, then the solution was injected into a helical microchannel device with or without a sheath flow. The MNC-EC complexes were separated from the free MNCs via the Dean drag force and lift force, and the separation was facilitated in the presence of a sheath flow. The concentration of the E. coli bacteria was determined using a light absorption spectrometer, and the limit of detection was found to be 10 cfu/mL in buffer solution and 100 cfu/mL in milk.
Utilization of abundant and cheap carbon sources can effectively reduce the production cost and enhance the economic feasibility. Acetate is a promising carbon source to achieve cost-effective microbial processes. In this study, we engineered an Escherichia coli strain to produce itaconic acid from acetate. As acetate is known to inhibit cell growth, we initially screened for a strain with a high tolerance to 10 g/L of acetate in the medium, and the W strain was selected as the host. Subsequently, the WC strain was obtained by overexpression of cad (encoding cis-aconitate decarboxylase) using a synthetic promoter and 5' UTR. However, the WC strain produced only 0.13 g/L itaconic acid because of low acetate uptake. To improve the production, the acetate assimilating pathway and glyoxylate shunt pathway were amplified by overexpression of pathway genes as well as its deregulation. The resulting strain, WCIAG4 produced 3.57 g/L itaconic acid (16.1% of theoretical maximum yield) after 88 hr of fermentation with rapid acetate assimilation. These efforts support that acetate can be a potential feedstock for biochemical production with engineered E. coli.
An extension of directed evolution strategies to genome-wide variations increases the chance of obtaining metabolite-overproducing microbes. However, a general high-throughput screening platform for selecting improved strains remains out of reach. Here, to expedite the evolution of metabolite-producing microbes, we utilize synthetic RNA devices comprising a riboswitch and a selection module that specifically sense inconspicuous metabolites. Using L-lysine-producing Escherichia coli as a model system, we demonstrated that this RNA device could enrich pathway-optimized strains to up to 75% of the total population after four rounds of enrichment cycles. Furthermore, the potential applicability of this device was examined by successfully extending its application to the case of L-tryptophan. When used in conjunction with combinatorial mutagenesis for metabolite overproduction, our synthetic RNA device should facilitate strain improvement.
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