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.

Encoding Genetic Circuits with DNA Barcodes Paves the Way for High-Throughput Profiling of Dose-Response Curves of Metabolite Biosensors.

Metabolite biosensors, through which the intracellular metabolite concentrations could be converted to changes in gene expression, are widely used in a variety of applications according to the different output signals. However, it remains challenging to fine-tune the dose-response relationships of biosensors to meet the needs of various scenarios. On the other hand, the short read length of next-generation sequencing (NGS) has greatly limited the design capability of sequence libraries. To address these issues, we describe a DNA trackable assembly method, coupled with fluorescence-activated cell sorting and NGS (Sort-Seq), to achieve the characterization of dose-response curves in a massively parallel manner. As a proof of the concept, we constructed a malonyl-CoA biosensor library containing 5184 combinations with six levels of transcription factor dosage, four different operator positions, and 216 possible upstream enhancer sequence (UAS) designs in Saccharomyces cerevisiae BY4700. By using Sort-Seq and machine learning approach, we obtained comprehensive dose-response relationships of the combinatorial sequence space. Therefore, 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.

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.

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.

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.

Guide-target mismatch effects on dCas9-sgRNA binding activity in living bacterial cells.

As an effective programmable DNA targeting tool, CRISPR-Cas9 system has been adopted in varieties of biotechnological applications. However, the off-target effects, derived from the tolerance towards guide-target mismatches, are regarded as the major problems in engineering CRISPR systems. To understand this, we constructed two sgRNA libraries carrying saturated single- and double-nucleotide mismatches in living bacteria cells, and profiled the comprehensive landscape of in vivo binding affinity of dCas9 toward DNA target guided by each individual sgRNA with particular mismatches. We observed a synergistic effect in seed, where combinatorial double mutations caused more severe activity loss compared with the two corresponding single mutations. Moreover, we found that a particular mismatch type, dDrG (D = A, T, G), only showed moderate impairment on binding. To quantitatively understand the causal relationship between mismatch and binding behaviour of dCas9, we further established a biophysical model, and found that the thermodynamic properties of base-pairing coupled with strand invasion process, to a large extent, can account for the observed mismatch-activity landscape. Finally, we repurposed this model, together with a convolutional neural network constructed based on the same mechanism, as a predictive tool to guide the rational design of sgRNA in bacterial CRISPR interference.

Detection of inorganic ions and organic molecules with cell-free biosensing systems.

Efforts to use genetically modified cells to detect inorganic ions and organic molecules have been hindered by biosafety concerns, limitation of cell membrane barriers, cytotoxicity problems, and unfriendly operations. To address those challenges, cell-free biosensing systems were established to detect arsenic, mercury, acyl-homoserine lactone, and benzoic acid in this study. The cell-free system mimics biological transcription and translation activities in an open environment without living cells. The most bioactive cell-free system was obtained by screening different Escherichia coli strain extracts and Mg2+ concentrations. It was found that the sensitivity of cell-free systems could reach nanomolar levels with the short response time. The selectivity experiments showed that cell-free biosensing of inorganic ions had no signal interference. Cell-free biosensing systems, with their wide range of molecule detection, short response time and high specificity, could potentially serve as powerful biosensing platforms for environmental and medical applications.

Recombinant Spidroins Fully Replicate Primary Mechanical Properties of Natural Spider Silk.

Despite significant efforts to engineer their heterologous production, recombinant spider silk proteins (spidroins) have yet to replicate the unparalleled combination of high strength and toughness exhibited by natural spider silks, preventing their use in numerous mechanically demanding applications. To overcome this long-standing challenge, we have developed a synthetic biology approach combining standardized DNA part assembly and split intein-mediated ligation to produce recombinant spidroins of previously unobtainable size (556 kDa), containing 192 repeat motifs of the Nephila clavipes dragline spidroin. Fibers spun from our synthetic spidroins are the first to fully replicate the mechanical performance of their natural counterparts by all common metrics, i.e., tensile strength (1.03 ± 0.11 GPa), modulus (13.7 ± 3.0 GPa), extensibility (18 ± 6%), and toughness (114 ± 51 MJ/m3). The developed process reveals a path to more dependable production of high-performance silks for mechanically demanding applications while also providing a platform to facilitate production of other high-performance natural materials.