Rational construction of synthetic consortia: Key considerations and model-based methods for guiding the development of a novel biosynthesis platform.

The rapid development of synthetic biology has significantly improved the capabilities of mono-culture systems in converting different substrates into various value-added bio-chemicals through metabolic engineering. However, overexpression of biosynthetic pathways in recombinant strains can impose a heavy metabolic burden on the host, resulting in imbalanced energy distribution and negatively affecting both cell growth and biosynthesis capacity. Synthetic consortia, consisting of two or more microbial species or strains with complementary functions, have emerged as a promising and efficient platform to alleviate the metabolic burden and increase product yield. However, research on synthetic consortia is still in its infancy, with numerous challenges regarding the design and construction of stable synthetic consortia. This review provides a comprehensive comparison of the advantages and disadvantages of mono-culture systems and synthetic consortia. Key considerations for engineering synthetic consortia based on recent advances are summarized, and simulation and computational tools for guiding the advancement of synthetic consortia are discussed. Moreover, further development of more efficient and cost-effective synthetic consortia with emerging technologies such as artificial intelligence and machine learning is highlighted.

Co-Cultivated Enzyme Constraint Metabolic Network Model for Rational Guidance in Constructing Synthetic Consortia to Achieve Optimal Pathway Allocation Prediction.

Synthetic consortia have emerged as a promising biosynthetic platform that offers new opportunities for biosynthesis. Genome-scale metabolic network models (GEMs) with complex constraints are extensively utilized to guide the synthesis in monocultures. However, few methods are currently available to guide the rational construction of synthetic consortia for predicting the optimal allocation strategy of synthetic pathways aimed at enhancing product synthesis. A standardized method to construct the co-cultivated Enzyme Constraint metabolic network model (CulECpy) is proposed, which integrates enzyme constraints and modular interaction scale constraints based on the research concept of "independent + global". This method is applied to construct several synthetic consortia models, which encompassed different target products, strains, synthetic pathways, and compositional structures. Analyzing the model, the optimal pathway allocation and initial inoculum ratio that enhance the synthesis of target products by synthetic consortia are predicted and verified. When comparing with the constructed co-culture synthesis system, the normalized root mean square error of all optimal theoretical yield simulations is found to be less than or equal to 0.25. The analyses and verifications demonstrate that the method CulECpy can guide the rational construction of synthetic consortia systems to facilitate biochemical synthesis.

Genotoxicity Assessment of Haloacetaldehyde Disinfection Byproducts via a Simplified Yeast-Based Toxicogenomics Assay.

Haloacetaldehydes (HALs) represent the third-largest category of disinfection byproducts (DBPs) in drinking water in terms of weight. As a subset of unregulated DBPs, only a few HALs have undergone assessment, yielding limited information regarding their genotoxicity mechanisms. Herein, we developed a simplified yeast-based toxicogenomics assay to evaluate the genotoxicity of five specific HALs. This assay recorded the protein expression profiles of eight Saccharomyces cerevisiae strains fused with green fluorescent protein, including all known DNA damage and repair pathways. High-resolution real-time pathway activation data and protein expression profiles in conjunction with clustering analysis revealed that the five HALs induced various DNA damage and repair pathways. Among these, chloroacetaldehyde and trichloroacetaldehyde were found to be positively associated with genotoxicity, while dichloroacetaldehyde, bromoacetaldehyde, and tribromoacetaldehyde displayed negative associations. The protein effect level index, which are molecular end points derived from a toxicogenomics assay, exhibited a statistically significant positive correlation with the results of traditional genotoxicity assays, such as the comet assay (rp = 0.830 and p < 0.001) and SOS/umu assay (rp = 0.786 and p = 0.004). This yeast-based toxicogenomics assay, which employs a minimal set of gene biomarkers, can be used for mechanistic genotoxicity screening and assessment of HALs and other chemical compounds. These results contribute to bridging the knowledge gap regarding the molecular mechanisms underlying the genotoxicity of HALs and enable the categorization of HALs based on their distinct DNA damage and repair mechanisms.

Field-deployable assay based on CRISPR-Cas13a coupled with RT-RPA in one tube for the detection of SARS-CoV-2 in wastewater.

CRISPR-based nucleic acid detection is easy to implement, field deployable, and always coupled with isothermal amplification to improve the sensitivity. However, the conventional detection requires two separate steps, which can cause long-lasting amplicon aerosol contaminants, hence leading to false-positive results. To address this problem, we proposed a one-tube assay based on CRISPR-Cas13a coupled with reverse transcription-recombinase polymerase amplification to avoid aerosol pollution. The one-tube assay could be completed within 40 min with a sensitivity of up to 180 copies of RNA per reaction, and exhibited no cross reactivity with two related coronaviruses. Our technology showed reproducibility with relative standard deviation of 4.6% responding to 1 fM nucleic acid for three times. It could be used to detect SARS-CoV-2 nucleic acids in raw wastewater with a limit of detection of 103 copies/mL. We also validated the practicability of this technique for viral detection in environmental water samples by detecting SARS-CoV-2 in wastewater, which were not detectable by RT-qPCR technology, showing resistance of this technology to wastewater matrix. It is anticipated that the robustness and high sensitivity will significantly promote the development of a point-of-care method in environmental virus monitoring.

Review of available "extraction + purification" methods of natural ceramides and their feasibility for sewage sludge analysis.

Natural ceramide, a biologically active compound present in plants, has been used widely in food, cosmetics, and pharmaceutical industries. Abundant ceramide has been detected in sewage sludge, which has inspired the idea to recycle ceramide from it. Therefore, the methods of extracting, purifying, and detecting ceramides from plants were reviewed, with the aim to establish methods to get condensed ceramide from sludge. Ceramide extraction methods include traditional methods (maceration, reflux, and Soxhlet extraction) and green technologies (ultrasound-assisted, microwave-assisted, and supercritical fluid extraction). In the past two decades, more than 70% of the articles have used traditional methods. However, green extraction methods are gradually improved and showed high extraction efficiency with lower solvent consumed. The preferred technique for ceramide purification is chromatography. Common solvent systems include chloroform-methanol, n-hexane-ethyl acetate, petroleum ether-ethyl acetate, and petroleum ether-acetone. For structural determination of ceramide, infrared spectroscopy, nuclear magnetic resonance spectroscopy, and mass spectrometry are used in combination. Among quantitative analysis methods for ceramide, liquid chromatography-mass spectrometry was the most accurate. This review concludes that with our prilemenary experiment results it is feasible to apply the plant "extraction + purification" process of ceramide to sludge, but more optimization need to be performed to get better results.

Nanozyme enhanced paper-based biochip with a smartphone readout system for rapid detection of cyanotoxins in water.

Cyanobacterial harmful algal blooms in freshwater systems can produce cyanotoxins, such as microcystins (MCs) and nodularins (NODs), presenting serious threats to human health and ecosystems. Required routine monitoring of cyanotoxins in water samples, as posed by U.S. EPA drinking water contaminant candidate list 5 (CCL5), demands for cost-effective, reliable and sensitive MCs/NODs detection methods. We report the development of a colorimetric paper-based immunochip assisted by nanozyme catalysis with a smartphone readout system for rapid detection of cyanotoxins in water. We show that the introduction of biorthogonal click reaction enables in situ facile self-assembly of multi-layers of peroxidase-like nanozyme onto the anti-MCs/NODs monoclonal antibody. We can detect 13 variants of MCs/NODs even in the sub-microgram per liter range with detection limit of below 0.7 µg/L and satisfactory recovery percentages between 88 and 120% in different water matrices. Our technology shows a good correlation with the well-developed ELISA technology, demonstrating its great potential applications in resource-limited or less-developed regions for on-site and large-scale screening of cyanotoxins in water environment.