Pooled CRISPR Interference Screening Identifies Crucial Transcription Factors in Gas-Fermenting Clostridium ljungdahlii.

Gas-fermenting Clostridium species hold tremendous promise for one-carbon biomanufacturing. To unlock their full potential, it is crucial to unravel and optimize the intricate regulatory networks that govern these organisms; however, this aspect is currently underexplored. In this study, we employed pooled CRISPR interference (CRISPRi) screening to uncover a wide range of functional transcription factors (TFs) in Clostridium ljungdahlii, a representative species of gas-fermenting Clostridium, with a special focus on TFs associated with the utilization of carbon resources. Among the 425 TF candidates, we identified 75 and 68 TF genes affecting the heterotrophic and autotrophic growth of C. ljungdahlii, respectively. We focused our attention on two of the screened TFs, NrdR and DeoR, and revealed their pivotal roles in the regulation of deoxyribonucleoside triphosphates (dNTPs) supply, carbon fixation, and product synthesis in C. ljungdahlii, thereby influencing the strain performance in gas fermentation. Based on this, we proceeded to optimize the expression of deoR in C. ljungdahlii by adjusting its promoter strength, leading to an improved growth rate and ethanol synthesis of C. ljungdahlii when utilizing syngas. This study highlights the effectiveness of pooled CRISPRi screening in gas-fermenting Clostridium species, expanding the horizons for functional genomic research in these industrially important bacteria.

Identification of the mutual gliding locus as a factor for gut colonization in non-native bee hosts using the ARTP mutagenesis.

BACKGROUND: The gut microbiota and their hosts profoundly affect each other's physiology and evolution. Identifying host-selected traits is crucial to understanding the processes that govern the evolving interactions between animals and symbiotic microbes. Current experimental approaches mainly focus on the model bacteria, like hypermutating Escherichia coli or the evolutionary changes of wild stains by host transmissions. A method called atmospheric and room temperature plasma (ARTP) may overcome the bottleneck of low spontaneous mutation rates while maintaining mild conditions for the gut bacteria. RESULTS: We established an experimental symbiotic system with gnotobiotic bee models to unravel the molecular mechanisms promoting host colonization. By in vivo serial passage, we tracked the genetic changes of ARTP-treated Snodgrassella strains from Bombus terrestris in the non-native honeybee host. We observed that passaged isolates showing genetic changes in the mutual gliding locus have a competitive advantage in the non-native host. Specifically, alleles in the orphan mglB, the GTPase activating protein, promoted colonization potentially by altering the type IV pili-dependent motility of the cells. Finally, competition assays confirmed that the mutations out-competed the ancestral strain in the non-native honeybee gut but not in the native host. CONCLUSIONS: Using the ARTP mutagenesis to generate a mutation library of gut symbionts, we explored the potential genetic mechanisms for improved gut colonization in non-native hosts. Our findings demonstrate the implication of the cell mutual-gliding motility in host association and provide an experimental system for future study on host-microbe interactions. Video Abstract.

Strain engineering of Bacillus coagulans with high osmotic pressure tolerance for effective L-lactic acid production from sweet sorghum juice under unsterile conditions.

Open unsterile fermentation of the low-cost non-food crop, sweet sorghum, is an economically feasible lactic acid biosynthesis process. However, hyperosmotic stress inhibits microbial metabolism and lactic acid biosynthesis, and engineering strains with high osmotic tolerance is challenging. Herein, heavy ion mutagenesis combined with osmotic pressure enrichment was used to engineer a hyperosmotic-tolerant Bacillus coagulans for L-lactic acid production. The engineered strain had higher osmotic pressure tolerance, when compared with the parental strain, primarily owing to its improved properties such as cell viability, cellular antioxidant capacity, and NADH supply. In a pilot-scale open unsterile fermentation using sweet sorghum juice as a feedstock, the engineered strain produced 94 g/L L-lactic acid with a yield of 91 % and productivity of 6.7 g/L/h, and optical purity of L-lactic acid at the end of fermentation was 99.8 %. In short, this study provided effective and low-cost approach to produce polymer-grade L-lactic acid.

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.

piR-368 promotes odontoblastic differentiation of dental papilla cells via the Smad1/5 signaling pathway by targeting Smurf1.

PURPOSE: The important role of non-coding RNAs in odontoblastic differentiation of dental tissue-derived stem cells has been widely demonstrated; however, whether piRNA (a subclass of non-coding RNA) involved in the course of odontoblastic differentiation is not yet available. This study aimed to investigate the expression profile of piRNA during odontogenic differentiation of mDPCs and the potential molecular mechanism in vitro. MATERIALS AND METHODS: The primary mouse dental papilla cells (mDPCs) were isolated from the first molars of 1-day postnatal Kunming mice. Then, they were cultured in odontogenic medium for 9 days. The expression profile of piRNA was detected by Small RNA sequencing. RT-qPCR was used to verify the elevation of piR-368. The mRNA and protein levels of mineralization markers were examined by qRT-PCR and Western blot analysis. Alkaline phosphatase (ALP) activity and alizarin red S staining were conducted to assess the odontoblastic differentiation ability. RESULTS: We validated piR-368 was significantly upregulated and interference with piR-368 markedly inhibited the odontogenic differentiation of mDPCs. In addition, the relationship between Smad1/5 signaling pathway and piR-368-induced odontoblastic differentiation has been discovered. Finally, we demonstrated Smurf1 as a target gene of piR-368 using dual-luciferase assays. CONCLUSION: This study was the first to illustrate the participation of piRNA in odontoblastic differentiation. We proved that piR-368 promoted odontoblastic differentiation of mouse dental papilla cells via the Smad1/5 signaling pathway by targeting Smurf1.

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.

Exploring the Trans-Cleavage Activity with Rolling Circle Amplification for Fast Detection of miRNA.

MicroRNAs (miRNAs) are a class of endogenous short noncoding RNA. They regulate gene expression and function, essential to biological processes. It is necessary to develop an efficient detection method to determine these valuable biomarkers for the diagnosis of cancers. In this paper, we proposed a general and rapid method for sensitive and quantitative detection of miRNA by combining CRISPR-Cas12a and rolling circle amplification (RCA) with the precircularized probe. Eventually, the detection of miRNA-21 could be completed in 70 min with a limit of detection of 8.1 pM with high specificity. The reaction time was reduced by almost 4 h from more than 5 h to 70 min, which makes detection more efficient. This design improves the efficiency of CRISPR-Cas and RCA-based sensing strategy and shows great potential in lab-based detection and point-of-care test.

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.

Breeding a novel chlorophyll-deficient mutant of Auxenochlorella pyrenoidosa for high-quality protein production by atmospheric room temperature plasma mutagenesis.

In the present work, a novel chlorophyll-deficient mutant of Auxenochlorella pyrenoidosa named A4-1 was generated by atmospheric room temperature plasma (ARTP) mutagenesis. Compared to the green wild type (WT) strain, the A4-1 mutant cultured in the dark displayed yellow colour with a 118-fold decrease of chlorophyll a and no detected chlorophyll b. Higher contents of protein (44.22 % DW), total amino acids (AAs, 34.84 % DW) and essential AAs (17.50 % DW) were also achieved, showing 31 %, 22 % and 30 % increases compared to the WT, respectively (p < 0.05). Metabolite profile analysis revealed that the chlorophyll biosynthesis pathway in the A4-1 mutant was probably inhibited in the dark, while more carbon skeletons might be utilized for de novo AAs synthesis. These results demonstrated that the A4-1 mutant not only has extremely low chlorophyll content, but also has higher protein content, making it a very promising candidate to produce microalgal protein for future foods.

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.

CRISPR-Cas12a-assisted elimination of the non-specific signal from non-specific amplification in the Exponential Amplification Reaction.

Non-specific amplification is a major problem in nucleic acid amplification resulting in false-positive results, especially for exponential amplification reactions (EXPAR). Although efforts were made to suppress the influence of non-specific amplification, such as chemical blocking of the template's 3'-ends and sequence-independent weakening of template-template interactions, it is still a common problem in many conventional EXPAR reactions. In this study, we propose a novel strategy to eliminate the non-specific signal from non-specific amplification by integrating the CRISPR-Cas12a system into two-templates EXPAR. An EXPAR-Cas12a strategy named EXPCas was developed, where the Cas12a system acted as a filter to filter out non-specific amplificons in EXPAR, suppressing and eliminating the influence of non-specific amplification. As a result, the signal-to-background ratio was improved from 1.3 to 15.4 using this method. With microRNA-21 (miRNA-21) as a target, the detection can be finished in 40 min with a LOD of 103 fM and no non-specific amplification was observed.

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.

Demonstration of a New Characterization Method for Weak Measurement.

In this work, the difference between the weak measurement method and the weak value amplification process and the classical measurement process is thoroughly discussed, and the transition conditions of the weak value enhancement are obtained. A transition mode of the weak measurement and the classical measurement is proposed for the first time, and a better fitting model of the measurement results is found by performing a systematic analysis. On top of that, the importance of the new fitting method for the application of the weak measurement system is verified during the industrial production of organic molecular -nucleic acid, protein, polysaccharide-hydrolysis or synthesis. At the same time, a variety of spectral characterization methods are proposed and the advantages and disadvantages of the different characterization methods are analyzed through carrying out experiments. Consequently, the wide implementation of weak measurement-based detection technology is attained.

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.