Base editor-mediated large-scale screening of functional mutations in bacteria for industrial phenotypes.

Base editing, the targeted introduction of point mutations into cellular DNA, holds promise for improving genome-scale functional genome screening to single-nucleotide resolution. Current efforts in prokaryotes, however, remain confined to loss-of-function screens using the premature stop codons-mediated gene inactivation library, which falls far short of fully releasing the potential of base editors. Here, we developed a base editor-mediated functional single nucleotide variant screening pipeline in Escherichia coli. We constructed a library with 31,123 sgRNAs targeting 462 stress response-related genes in E. coli, and screened for adaptive mutations under isobutanol and furfural selective conditions. Guided by the screening results, we successfully identified several known and novel functional mutations. Our pipeline might be expanded to the optimization of other phenotypes or the strain engineering in other microorganisms.

Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision.

Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, are closely related to cellular behavior. However, quantitative description of these characteristics has so far relied on arrayed methods, which are time-consuming and labor-intensive. To address this issue, we propose a deep-learning-assisted Sort-Seq approach (dSort-Seq) in this work, enabling high-throughput profiling of expression properties with high precision. We demonstrated the validity of dSort-Seq for large-scale assaying of the dose-response relationships of biosensors. In addition, we comprehensively investigated the contribution of transcription and translation to noise production in Escherichia coli, from which we found that the expression noise is strongly coupled with the mean expression level. We also found that the transcriptional interference caused by overlapping RpoD-binding sites contributes to noise production, which suggested the existence of a simple and feasible noise control strategy in E. coli.

Exploration of DPP-IV Inhibitory Peptide Design Rules Assisted by the Deep Learning Pipeline That Identifies the Restriction Enzyme Cutting Site.

The mining of antidiabetic dipeptidyl peptidase IV (DPP-IV) inhibitory peptides (DPP-IV-IPs) is currently a costly and laborious process. Due to the absence of rational peptide design rules, it relies on cumbersome screening of unknown enzyme hydrolysates. Here, we present an enhanced deep learning model called bidirectional encoder representation (BERT)-DPPIV, specifically designed to classify DPP-IV-IPs and explore their design rules to discover potent candidates. The end-to-end model utilizes a fine-tuned BERT architecture to extract structural/functional information from input peptides and accurately identify DPP-IV-Ips from input peptides. Experimental results in the benchmark data set showed BERT-DPPIV yielded state-of-the-art accuracy and MCC of 0.894 and 0.790, surpassing the 0.797 and 0.594 obtained by the sequence-feature model. Furthermore, we leveraged the attention mechanism to uncover that our model could recognize the restriction enzyme cutting site and specific residues that contribute to the inhibition of DPP-IV. Moreover, guided by BERT-DPPIV, proposed design rules for DPP-IV inhibitory tripeptides and pentapeptides were validated, and they can be used to screen potent DPP-IV-IPs.

Mining and Validation of Novel Hemp Seed-Derived DPP-IV-Inhibiting Peptides Using a Combination of Multi-omics and Molecular Docking.

Hemp seed-derived inhibitors of dipeptidyl peptidase IV (DPP-IV) demonstrate potential as novel therapeutics for diabetes; however, their proteome and genome remain uncharacterized. We used multi-omics technology to mine peptides capable of inhibiting DPP-IV. First, 1261 and 1184 proteins were identified in fresh and dry hemp seeds, respectively. Simulated protease cleavage of dry seed proteins yielded 185,446 peptides for virtual screening to select the potential DPP-IV-inhibiting peptides. Sixteen novel peptides were selected according to their DPP-IV-binding affinity determined via molecular docking. In vitro DPP-IV inhibition assays identified the peptides LPQNIPPL, YPYY, YPW, LPYPY, WWW, YPY, YPF, and WS with half-maximal inhibitory concentration (IC50) values lower than 0.5 mM, which were 0.08 ± 0.01, 0.18 ± 0.03, 0.18 ± 0.01, 0.20 ± 0.03, 0.22 ± 0.03, 0.29 ± 0.02, 0.42 ± 0.03, and 0.44 ± 0.09 mM, respectively. The dissociation constants (KD) of the 16 peptides ranged from 1.50 × 10-4 to 1.82 × 10-7 M. Furthermore, Caco2 and INS-1 cell assays showed that all 16 peptides could efficiently inhibit DPP-IV activity and increase insulin and glucagon-like peptide-1 concentrations. These results demonstrate a well-established and efficient method to isolate food-derived therapeutic DPP-IV-inhibiting peptides.

CRISPRi-microfluidics screening enables genome-scale target identification for high-titer protein production and secretion.

Genome-scale target identification promises to guide microbial cell factory engineering for higher-titer production of biomolecules such as recombinant proteins (r-protein), but challenges remain due to the need not only for comprehensive genotypic perturbation but also in conjunction with high-throughput phenotypic screening strategies. Here, we developed a CRISPRi-microfluidics screening platform to systematically identify crucial gene targets that can be engineered to enhance r-protein secretion in Corynebacterium glutamicum. We created a CRISPR interference (CRISPRi) library containing 46,549 single-guide RNAs, where we aimed to unbiasedly target all genes for repression. Meanwhile, we developed a highly efficient droplet-based microfluidics system integrating the FlAsH-tetracysteine assay that enables screening of millions of strains to identify potential knockdowns conducive to nanobody VHH secretion. Among our highest-ranking candidates are a slew of previously unknown targets involved in transmembrane transport, amino-acid metabolism and redox regulation. Guided by these findings, we eventually constructed a hyperproducer for multiple proteins via combinatorial engineering of redox-response transcription factors. As the near-universal applicability of CRISPRi technology and the FlAsH-based screening platform, this procedure might be expanded to include a varied variety of microbial species and recombinant proteins.

Sodium formate redirects carbon flux and enhances heterologous mevalonate production in Methylobacterium extorquens AM1.

Methylobacterium extorquens AM1 (AM1), a model strain of methylotrophic cell factories (MeCFs) could be used to produce fine chemicals from methanol. Synthesis of heterologous products usually needs reducing cofactors, but AM1 growing on methanol lack reducing power. Formate could be used as a reducing agent. In this study, mevalonic acid (MEV) yield of 0.067 gMEV/g methanol was reached by adding 10 mmol L-1 sodium formate in MEV accumulating stage (at 72 h). The yield was improved by 64.57%, and represented the highest yield reported to date. 13 C-labeling experiments revealed global effects of sodium formate on metabolic pathways in engineered Methylobacterium extorquens AM1. Sodium formate significantly increased the ratios of reducing equivalents, enhanced the metabolic rate of pathways demanding reducing cofactors and redirected the carbon flux to MEV synthesis. As a result, coupling formate to methanol-based production provide a promising way for converting C1 substances to useful chemical products.

Single-cell microliter-droplet screening system (MISS Cell): An integrated platform for automated high-throughput microbial monoclonal cultivation and picking.

Solid plates have been used for microbial monoclonal isolation, cultivation, and colony picking since 1881. However, the process is labor- and resource-intensive for high-throughput requirements. Currently, several instruments have been integrated for automated and high-throughput picking, but complicated and expensive. To address these issues, we report a novel integrated platform, the single-cell microliter-droplet screening system (MISS Cell), for automated, high-throughput microbial monoclonal colony cultivation and picking. We verified the monoclonality of droplet cultures in the MISS Cell and characterized culture performance. Compared with solid plates, the MISS Cell generated a larger number of monoclonal colonies with higher initial growth rates using fewer resources. Finally, we established a workflow for automated high-throughput screening of Corynebacterium glutamicum using the MISS Cell and identified high glutamate-producing strains. The MISS Cell can serve as a universal platform to efficiently produce monoclonal colonies in high-throughput applications, overcoming the limitations of solid plates to promote rapid development in biotechnology.

Turning C1-gases to isobutanol towards great environmental and economic sustainability via innovative biological routes: two birds with one stone.

BACKGROUND: The dramatic increase in greenhouse gas (GHG) emissions, which causes serious global environmental issues and severe climate changes, has become a global problem of concern in recent decades. Currently, native and/or non-native C1-utilizing microbes have been modified to be able to effectively convert C1-gases (biogas, natural gas, and CO2) into isobutanol via biological routes. Even though the current experimental results are satisfactory in lab-scale research, the techno-economic feasibility of C1 gas-derived isobutanol production at the industrial scale still needs to be analyzed and evaluated, which will be essential for the future industrialization of C1-gas bioconversion. Therefore, techno-economic analyses were conducted in this study with comparisons of capital cost (CAPEX), operating cost (OPEX), and minimum isobutanol selling price (MISP) derived from biogas (scenario #1), natural gas (scenario #2), and CO2 (scenario #3) with systematic economic assessment. RESULTS: By calculating capital investments and necessary expenses, the highest CAPEX ($317 MM) and OPEX ($67 MM) were projected in scenario #1 and scenario #2, respectively. Because of the lower CAPEX and OPEX from scenario #3, the results revealed that bioconversion of CO2 into isobutanol temporally exhibited the best economic performance with an MISP of $1.38/kg isobutanol. Furthermore, a single sensitivity analysis with nine different parameters was carried out for the production of CO2-derived isobutanol. The annual plant capacity, gas utilization rate, and substrate cost are the three most important economic-driving forces on the MISP of CO2-derived isobutanol. Finally, a multiple-point sensitivity analysis considering all five parameters simultaneously was performed using ideal targets, which presented the lowest MISP of $0.99/kg in a long-term case study. CONCLUSIONS: This study provides a comprehensive assessment of the bioconversion of C1-gases into isobutanol in terms of the bioprocess design, mass/energy calculation, capital investment, operating expense, sensitivity analysis, and minimum selling price. Compared with isobutanol derived from biogas and natural gas, the CO2-based isobutanol showed better economic feasibility. A market competitive isobutanol derived from CO2 is predicable with lower CO2 cost, better isobutanol titer, and higher annual capacity. This study will help researchers and decision-makers explore innovative and effective approaches to neutralizing GHGs and focus on key economic-driving forces to improve techno-economic performance.

Automated Microbial Cultivation and Adaptive Evolution using Microbial Microdroplet Culture System (MMC).

Conventional microbial cultivation methods usually have cumbersome operations, low throughput, low efficiency, and large consumption of labor and reagents. Moreover, microplate-based high-throughput cultivation methods developed in recent years have poor microbial growth status and experiment parallelization because of their low dissolved oxygen, poor mixture, and severe evaporation and thermal effect. Due to many advantages of micro-droplets, such as small volume, high throughput, and strong controllability, the droplet-based microfluidic technology can overcome these problems, which has been used in many kinds of research of high-throughput microbial cultivation, screening, and evolution. However, most prior studies remain at the stage of laboratory construction and application. Some key issues, such as high operational requirements, high construction difficulty, and lack of automated integration technology, restrict the wide application of droplet microfluidic technology in microbial research. Here, an automated Microbial Microdroplet Culture system (MMC) was successfully developed based on droplet microfluidic technology, achieving the integration of functions such as inoculation, cultivation, online monitoring, sub-cultivation, sorting, and sampling required by the process of microbial droplet cultivation. In this protocol, wild-type Escherichia coli (E. coli) MG1655 and a methanol-essential E. coli strain (MeSV2.2) were taken as examples to introduce how to use the MMC to conduct automated and relatively high-throughput microbial cultivation and adaptive evolution in detail. This method is easy to operate, consumes less labor and reagents, and has high experimental throughput and good data parallelity, which has great advantages compared with conventional cultivation methods. It provides a low-cost, operation-friendly, and result-reliable experimental platform for scientific researchers to conduct related microbial research.

Encoding Genetic Circuits with DNA Barcodes Paves the Way for Machine Learning-Assisted Metabolite Biosensor Response Curve Profiling in Yeast.

Genetically encoded biosensors are valuable tools used in the precise engineering of metabolism. Although a large number of biosensors have been developed, the fine-tuning of their dose-response curves, which promotes the applications of biosensors in various scenarios, still remains challenging. To address this issue, we leverage a DNA trackable assembly method and fluorescence-activated cell sorting coupled with next-generation sequencing (FACS-seq) technology to set up a novel workflow for construction and comprehensive characterization of thousands of biosensors in a massively parallel manner. An FapR-fapO-based malonyl-CoA biosensor was used as proof of concept to construct a trackable combinatorial library, containing 5184 combinations with 6 levels of transcription factor dosage, 4 different operator positions, and 216 possible upstream enhancer sequence (UAS) designs. By applying the FACS-seq technique, the response curves of 2632 biosensors out of 5184 combinations were successfully characterized to provide large-scale genotype-phenotype association data of the designed biosensors. Finally, machine-learning algorithms were applied to predict the genotype-phenotype relationships of the uncharacterized combinations to generate a panoramic scanning map of the combinatorial space. With the assistance of our novel workflow, a malonyl-CoA biosensor with the largest dynamic response range was successfully obtained. Moreover, feature importance analysis revealed that the recognition sequence insertion scheme and the choice of UAS have a significant impact on the dynamic range. Taken together, our pipeline provides a platform for the design, tuning, and profiling of biosensor response curves and shows great potential to facilitate the rational design of genetic circuits.

New Method for Genome-Scale Functional Genomic Study in Bacteria with Superior Performance: CRISPR Interference Screen.

High-throughput genetic screens based on CRISPR/Cas9 technology are powerful tools to genome-wide identify gene function and genotype-phenotype association. Here, we describe a detailed protocol for conducting and evaluating pooled CRISPR screens interfering with gene expression in Escherichia coli. We provide step-by-step instructions for guide RNA library design and construction, genome-scale screening and next-generation sequencing data processing. This tool outperforms transposon sequencing (Tn-seq) with similar library sizes and short gene length. The workflow can be used in follow-up studies implemented in other bacteria systems.

Construction and characterization of novel bifunctional fusion proteins composed of alcohol dehydrogenase and NADH oxidase with efficient oxidized cofactor regeneration.

To tune the efficiency of oxidized cofactor recycling between alcohol dehydrogenase (ADH) and NADH oxidase (NOX) for the production of aromatic chiral alcohols, we designed and constructed four novel bifunctional fusion proteins composed of thermostable ADH and NOX from Thermococcus kodakarensis KOD1. ADH was linked to the N- or C-terminus of NOX with a typical rigid linker (EAAAK)3 and a flexible linker (GGGGS)3 , respectively. Compared with the parental enzymes, the NOX moieties in the four fusion proteins exhibited higher specific activities (141%-282%), while the ADH moieties exhibited varying levels of specific activity (69%-167%). All fusion proteins showed decreased affinities toward the cofactors, with increased Km values toward NADH (159%-406%) and NAD+ (202%-372%). In the enantioselective oxidation of (RS)-1-phenylethanol coupled with cofactor regeneration, the four fusion proteins displayed different positive and negative effects on the recycling efficiency of the oxidized cofactor. The two fusion proteins composed of NOX at the N-terminus exhibited higher total turnover numbers than the corresponding mixtures of individual enzymes with equal activities, particularly at low cofactor concentrations. These findings suggest high cofactor recycling efficiencies of the fusion proteins with appropriate design and their potential application in the biosynthesis of chiral alcohols.

Advanced strategies and tools to facilitate and streamline microbial adaptive laboratory evolution.

Adaptive laboratory evolution (ALE) has served as a historic microbial engineering method that mimics natural selection to obtain desired microbes. The past decade has witnessed improvements in all aspects of ALE workflow, in terms of growth coupling, genotypic diversification, phenotypic selection, and genotype-phenotype mapping. The developing growth-coupling strategies facilitate ALE to a wider range of appealing traits. In vivo mutagenesis methods and multiplexed automated culture platforms open new gates to streamline its execution. Meanwhile, the application of multi-omics analyses and multiplexed genetic engineering promote efficient knowledge mining. This article provides a comprehensive and updated review of these advances, highlights newest significant applications, and discusses future improvements, aiming to provide a practical guide for implementation of novel, effective, and efficient ALE experiments.

A versatile toolbox for CRISPR-based genome engineering in Pichia pastoris.

Pichia pastoris has gained much attention as a popular microbial cell factory for the production of recombinant proteins and high-value chemicals from laboratory to industrial scale. However, the lack of convenient and efficient genome engineering tools has impeded further applications of Pichia pastoris towards metabolic engineering and synthetic biology. Here, we report a CRISPR-based toolbox for gene editing and transcriptional regulation in P. pastoris. Based on the previous attempts in P. pastoris, we constructed a CRISPR/Cas9 system for gene editing using the RNA Pol-III-driven expression of sgRNA. The system was used to rapidly recycle the selectable marker with an eliminable episomal plasmid and achieved up to 100% knockout efficiency. Via dCas9 fused with transcriptional repressor (Mix1/RD1152) or activator (VPR), a flexible toolbox for regulation of gene expression was developed. The reporter gene eGFP driven by yeast pGAP or pCYC1 promoter showed strong inhibition (above 70%) and up to ~ 3.5-fold activation. To implement the combinatorial genetic engineering strategy, the CRISPR system contained a single Cas9-VPR protein, and engineered gRNA was introduced in P. pastoris for simultaneous gene activation, repression, and editing (CRISPR-ARE). We demonstrated that CRISPR-ARE was highly efficient for eGFP activation, mCherry repression, and ADE2 disruption, individually or in a combinatorial manner with a stable expression of multiplex sgRNAs. The simple and multifunctional toolkit demonstrated in this study will accelerate the application of P. pastoris in metabolic engineering and synthetic biology. KEY POINTS: • An eliminable CRISPR/Cas9 system yielded a highly efficient knockout of genes. • Simplified CRISPR/dCas9-based tools enabled transcriptional regulation of targeted genes. • CRISPR-ARE system achieved simultaneous gene activation, repression, and editing in P. pastoris.

Binding ability of methylene blue with heparin dependent on its sulfate level rather than its sulfation location or basic saccharide structure.

Methylene blue (MB) is one of the most common cationic dyes to detect heparin. As the sulfate residue presented in heparin was the main contributor to bind with MB, the UV performance of the MB with selectively desulfated heparin derivatives was investigated. It was found that the sulfate residue in different heparin analogues did not show the equal ability to attract MB binding. The stoichiometry of sulfate with MB among the heparin and derivatives was verified as a non-constant number. For the two selectively desulfated heparin derivatives: sulfate elimination at 6-O (6-OdeS) and N-acetylated heparin (N-deS-Acetyl), the MB to sulfate ratios were significantly higher than for heparin. For the not fully diminished sulfate at 2-O heparin derivative (2-OdeS), the MB-SO3- ratio of 2-OdeS was between 6-OdeS, N-deS-Acetlyl and heparin. Although in a distinct sulfation position, the MB-SO3- ratio of 6-OdeS and N-deS-Acetyl was almost equal, which agreed with the comparable total desulfation degree between 6-OdeS and N-deS-Acetyl. In addition, compared to heparin groups, the non-desulfated gs-HP showed no significantly different MB-SO3- ratio with heparin. The above results demonstrated that compared with the sulfate location and glycan composition of heparin, the content of sulfate was the most essential factor for the MB binding.

[Functional discovery and production technology for natural bioactive peptides].

Bioactive peptides play important roles in promoting human health, such as lowering blood pressure, blood sugar and blood lipid, anti-obesity, and anti-cancer. Thus, exploring functional bioactive peptides and developing efficient production technologies are of crucial importance. Herein, we review the development of function discovery and production technology for natural bioactive peptides. Presently, the top-down and bottom-up approaches are mainly used for the function discovery and production of natural active peptides. The top-down approach includes the direct extraction and identification for functional discovery, and the direct extraction, enzymatic hydrolysis and microbial fermentation for production. The bottom-up approach includes the polypeptide modification and database mining for functional discovery, and the chemical synthesis, enzyme synthesis, recombinant expression and cell-free synthesis for production. The top-down approach is usually associated with complicated process, lower efficiency, higher cost, harder quality control, and uncertain functionality, while the bottom-up approach is more suitable for the development of peptide drugs but difficult to be used for functional foods. With the technology development of sequencing and mass spectrometry, it is easier to obtain the proteomic information of various organisms at the molecular level. Based on the proteomic information, the top-down and bottom-up approaches can be combined to overcome the disadvantages of using these two approaches alone, thus providing a new strategy for the rapid development and production of natural active peptides.

Genome-wide genotype-phenotype associations in microbes.

The concept of a gene has been developed a lot since the Mendelian era owing to the rapid progress in molecular biology and informatics. To explore the nature of life, varieties of biological tools have been continuously established. Many achievements have been made to clarify the relationships between genotypes and phenotypes. However, it is still not completely clear that how traits of an organism are encoded by its genome. In this review, we will summarize and discuss representative works in systematical functional genomic studies in microbes. By analyzing their developmental progressions and limitations, we may have chances to design more powerful means to decipher the code of life.

[Technology development and instrumentation of a high-throughput and automated microbial microdroplet culture system for microbial evolution and screening].

Since microdroplets are able to be generated rapidly in large amount and each droplet can be well controlled as an independent micro-cultivator, droplet microfluidic technology can be potentially used in the culture of microorganisms, and provide the microbial culture with high throughput manner. But its application mostly stays in the laboratory-level building and using for scientific research, and the wide use of droplet microfluidics in microbial technology has been limited by the key problems that the operation for microdroplets needs high technical requirements with wide affecting factors and the difficulties in integration of automatic microdroplet instrumentation. In this study, by realizing and integrating the complicated operations of droplet generation, cultivation, detection, splitting, fusion and sorting, we design a miniaturized, fully automated and high-throughput microbial microdroplet culture system (MMC). The MMC can be widely used in microbial growth curve test, laboratory adaptive evolution, single factor and multi-level analysis of microbial culture, metabolite detection and so on, and provide a powerful instrument platform for customized microbial evolution and screening aiming at efficient strain engineering.

Effects of bioactive components of Pu-erh tea on gut microbiomes and health: A review.

Pu-erh tea is a post-fermentation tea with unique flavor and multiple health benefits. Due to the various microorganisms involved in the post-fermentation process, Pu-erh tea contains highly complex components, which have rich interactions with the gut microbiomes (GMs). Because the structure and homeostasis of GMs are closely related to human wellness and the various diseases progress, the beneficial effects of Pu-erh tea on GMs have a great potential for application in health care. However, there is no systematic summary of the bioactive components of Pu-erh tea, and their effects on the GMs. Here, we review the current studies on the effects of Pu-erh tea and its bioactive components on the structure of GMs as well as on health improvement, and further discuss the relevant quality indicators. This "components - function - indicators" clue will hopefully stimulate the standardization of Pu-erh tea fermentation process and the development of its functional products.