DNA glycosylases provide antiviral defence in prokaryotes.

Bacteria have adapted to phage predation by evolving a vast assortment of defence systems1. Although anti-phage immunity genes can be identified using bioinformatic tools, the discovery of novel systems is restricted to the available prokaryotic sequence data2. Here, to overcome this limitation, we infected Escherichia coli carrying a soil metagenomic DNA library3 with the lytic coliphage T4 to isolate clones carrying protective genes. Following this approach, we identified Brig1, a DNA glycosylase that excises α-glucosyl-hydroxymethylcytosine nucleobases from the bacteriophage T4 genome to generate abasic sites and inhibit viral replication. Brig1 homologues that provide immunity against T-even phages are present in multiple phage defence loci across distinct clades of bacteria. Our study highlights the benefits of screening unsequenced DNA and reveals prokaryotic DNA glycosylases as important players in the bacteria-phage arms race.

High-throughput retrieval of target sequences from complex clone libraries using CRISPRi.

The capture of metagenomic DNA in large clone libraries provides the opportunity to study microbial diversity that is inaccessible using culture-dependent methods. In this study, we harnessed nuclease-deficient Cas9 to establish a CRISPR counter-selection interruption circuit (CCIC) that can be used to retrieve target clones from complex libraries. Combining modern sequencing methods with CCIC cloning allows for rapid physical access to the genetic diversity present in natural ecosystems.

Multiplexed mobilization and expression of biosynthetic gene clusters.

Bacterial genomes contain large reservoirs of biosynthetic gene clusters (BGCs) that are predicted to encode unexplored natural products. Heterologous expression of previously unstudied BGCs should facilitate the discovery of additional therapeutically relevant bioactive molecules from bacterial culture collections, but the large-scale manipulation of BGCs remains cumbersome. Here, we describe a method to parallelize the identification, mobilization and heterologous expression of BGCs. Our solution simultaneously captures large numbers of BGCs by cloning the genomes of a strain collection in a large-insert library and uses the CONKAT-seq (co-occurrence network analysis of targeted sequences) sequencing pipeline to efficiently localize clones carrying intact BGCs which represent candidates for heterologous expression. Our discovery of several natural products, including an antibiotic that is active against multi-drug resistant Staphylococcus aureus, demonstrates the potential of leveraging economies of scale with this approach to systematically interrogate cryptic BGCs contained in strain collections.

Uncovering the biosynthetic potential of rare metagenomic DNA using co-occurrence network analysis of targeted sequences.

Sequencing of DNA extracted from environmental samples can provide key insights into the biosynthetic potential of uncultured bacteria. However, the high complexity of soil metagenomes, which can contain thousands of bacterial species per gram of soil, imposes significant challenges to explore secondary metabolites potentially produced by rare members of the soil microbiome. Here, we develop a targeted sequencing workflow termed CONKAT-seq (co-occurrence network analysis of targeted sequences) that detects physically clustered biosynthetic domains, a hallmark of bacterial secondary metabolism. Following targeted amplification of conserved biosynthetic domains in a highly partitioned metagenomic library, CONKAT-seq evaluates amplicon co-occurrence patterns across library subpools to identify chromosomally clustered domains. We show that a single soil sample can contain more than a thousand uncharacterized biosynthetic gene clusters, most of which originate from low frequency genomes which are practically inaccessible through untargeted sequencing. CONKAT-seq allows scalable exploration of largely untapped biosynthetic diversity across multiple soils, and can guide the discovery of novel secondary metabolites from rare members of the soil microbiome.

Sensing new chemicals with bacterial transcription factors.

Bacteria rely on allosteric transcription factors (aTFs) to sense a wide range of chemicals. The variety of effectors has contributed in making aTFs the most used input system in synthetic biological circuits. Considering their enabling role in biotechnology, an important question concerns the size of the chemical space that can potentially be detected by these biosensors. From digging into the ever changing repertoire of natural regulatory circuits, to advances in aTF engineering, we review here different strategies that are pushing the boundaries of this chemical space. We also review natural and synthetic cases of indirect sensing, where aTFs work in combination with metabolism to enable detection of new molecules.

SensiPath: computer-aided design of sensing-enabling metabolic pathways.

Genetically-encoded biosensors offer a wide range of opportunities to develop advanced synthetic biology applications. Circuits with the ability of detecting and quantifying intracellular amounts of a compound of interest are central to whole-cell biosensors design for medical and environmental applications, and they also constitute essential parts for the selection and regulation of high-producer strains in metabolic engineering. However, the number of compounds that can be detected through natural mechanisms, like allosteric transcription factors, is limited; expanding the set of detectable compounds is therefore highly desirable. Here, we present the SensiPath web server, accessible at http://sensipath.micalis.fr SensiPath implements a strategy to enlarge the set of detectable compounds by screening for multi-step enzymatic transformations converting non-detectable compounds into detectable ones. The SensiPath approach is based on the encoding of reactions through signature descriptors to explore sensing-enabling metabolic pathways, which are putative biochemical transformations of the target compound leading to known effectors of transcription factors. In that way, SensiPath enlarges the design space by broadening the potential use of biosensors in synthetic biology applications.

Phage-mediated Delivery of Targeted sRNA Constructs to Knock Down Gene Expression in E. coli.

RNA-mediated knockdowns are widely used to control gene expression. This versatile family of techniques makes use of short RNA (sRNA) that can be synthesized with any sequence and designed to complement any gene targeted for silencing. Because sRNA constructs can be introduced to many cell types directly or using a variety of vectors, gene expression can be repressed in living cells without laborious genetic modification. The most common RNA knockdown technology, RNA interference (RNAi), makes use of the endogenous RNA-induced silencing complex (RISC) to mediate sequence recognition and cleavage of the target mRNA. Applications of this technique are therefore limited to RISC-expressing organisms, primarily eukaryotes. Recently, a new generation of RNA biotechnologists have developed alternative mechanisms for controlling gene expression through RNA, and so made possible RNA-mediated gene knockdowns in bacteria. Here we describe a method for silencing gene expression in E. coli that functionally resembles RNAi. In this system a synthetic phagemid is designed to express sRNA, which may designed to target any sequence. The expression construct is delivered to a population of E. coli cells with non-lytic M13 phage, after which it is able to stably replicate as a plasmid. Antisense recognition and silencing of the target mRNA is mediated by the Hfq protein, endogenous to E. coli. This protocol includes methods for designing the antisense sRNA, constructing the phagemid vector, packaging the phagemid into M13 bacteriophage, preparing a live cell population for infection, and performing the infection itself. The fluorescent protein mKate2 and the antibiotic resistance gene chloramphenicol acetyltransferase (CAT) are targeted to generate representative data and to quantify knockdown effectiveness.

Expanding Biosensing Abilities through Computer-Aided Design of Metabolic Pathways.

Detection of chemical signals is critical for cells in nature as well as in synthetic biology, where they serve as inputs for designer circuits. Important progress has been made in the design of signal processing circuits triggering complex biological behaviors, but the range of small molecules recognized by sensors as inputs is limited. The ability to detect new molecules will increase the number of synthetic biology applications, but direct engineering of tailor-made sensors takes time. Here we describe a way to immediately expand the range of biologically detectable molecules by systematically designing metabolic pathways that transform nondetectable molecules into molecules for which sensors already exist. We leveraged computer-aided design to predict such sensing-enabling metabolic pathways, and we built several new whole-cell biosensors for molecules such as cocaine, parathion, hippuric acid, and nitroglycerin.

A Sense of Balance: Experimental Investigation and Modeling of a Malonyl-CoA Sensor in Escherichia coli.

Production of value-added chemicals in microorganisms is regarded as a viable alternative to chemical synthesis. In the past decade, several engineered pathways producing such chemicals, including plant secondary metabolites in microorganisms have been reported; upscaling their production yields, however, was often challenging. Here, we analyze a modular device designed for sensing malonyl-CoA, a common precursor for both fatty acid and flavonoid biosynthesis. The sensor can be used either for high-throughput pathway screening in synthetic biology applications or for introducing a feedback circuit to regulate production of the desired chemical. Here, we used the sensor to compare the performance of several predicted malonyl-CoA-producing pathways, and validated the utility of malonyl-CoA reductase and malonate-CoA transferase for malonyl-CoA biosynthesis. We generated a second-order dynamic linear model describing the relation of the fluorescence generated by the sensor to the biomass of the host cell representing a filter/amplifier with a gain that correlates with the level of induction. We found the time constants describing filter dynamics to be independent of the level of induction but distinctively clustered for each of the production pathways, indicating the robustness of the sensor. Moreover, by monitoring the effect of the copy-number of the production plasmid on the dose-response curve of the sensor, we managed to coarse-tune the level of pathway expression to maximize malonyl-CoA synthesis. In addition, we provide an example of the sensor's use in analyzing the effect of inducer or substrate concentrations on production levels. The rational development of models describing sensors, supplemented with the power of high-throughput optimization provide a promising potential for engineering feedback loops regulating enzyme levels to maximize productivity yields of synthetic metabolic pathways.

Silencing of antibiotic resistance in E. coli with engineered phage bearing small regulatory RNAs.

In response to emergent antibiotic resistance, new strategies are needed to enhance the effectiveness of existing antibiotics. Here, we describe a phagemid-delivered, RNA-mediated system capable of directly knocking down antibiotic resistance phenotypes. Small regulatory RNAs (sRNAs) were designed to specifically inhibit translation of chloramphenicol acetyltransferase and kanamycin phosphotransferase. Nonlytic phagemids coding for sRNA expression were able to infect and restore chloramphenicol and kanamycin sensitivity to populations of otherwise resistant E. coli. This modular system could easily be extended to other bacteria with resistance profiles that depend on specific transcripts.

In situ characterization of mycobacterial growth inhibition by lytic enzymes expressed in vectorized E. coli.

The emergence of extremely drug resistant Mycobacterium tuberculosis necessitates new strategies to combat the pathogen. Engineered bacteria may serve as vectors to deliver proteins to human cells, including mycobacteria-infected macrophages. In this work, we target Mycobacterium smegmatis, a nonpathogenic tuberculosis model, with E. coli modified to express trehalose dimycolate hydrolase (TDMH), a membrane-lysing serine esterase. We show that TDMH-expressing E. coli are capable of lysing mycobacteria in vitro and at low pH. Vectorized E. coli producing TDMH were found suppress the proliferation of mycobacteria in infected macrophages.