High‐throughput CRISPRi phenotyping identifies new essential genes in Streptococcus pneumoniae
2017; Springer Nature; Volume: 13; Issue: 5 Linguagem: Inglês
10.15252/msb.20167449
ISSN1744-4292
AutoresXue Liu, Clément Gallay, Morten Kjos, Arnau Domenech, Jelle Slager, Sebastiaan P. van Kessel, Kèvin Knoops, Robin A. Sorg, Jing‐Ren Zhang, Jan‐Willem Veening,
Tópico(s)Pneumonia and Respiratory Infections
ResumoArticle10 May 2017Open Access Transparent process High-throughput CRISPRi phenotyping identifies new essential genes in Streptococcus pneumoniae Xue Liu Xue Liu orcid.org/0000-0001-6485-1865 Molecular Genetics Group, Groningen Biomolecular Sciences and Biotechnology Institute, Centre for Synthetic Biology, University of Groningen, Groningen, The Netherlands Center for Infectious Disease Research, School of Medicine, Tsinghua University, Beijing, China Search for more papers by this author Clement Gallay Clement Gallay orcid.org/0000-0002-6296-8928 Molecular Genetics Group, Groningen Biomolecular Sciences and Biotechnology Institute, Centre for Synthetic Biology, University of Groningen, Groningen, The Netherlands Search for more papers by this author Morten Kjos Morten Kjos orcid.org/0000-0003-4448-9082 Molecular Genetics Group, Groningen Biomolecular Sciences and Biotechnology Institute, Centre for Synthetic Biology, University of Groningen, Groningen, The Netherlands Department of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway Search for more papers by this author Arnau Domenech Arnau Domenech orcid.org/0000-0002-0829-511X Molecular Genetics Group, Groningen Biomolecular Sciences and Biotechnology Institute, Centre for Synthetic Biology, University of Groningen, Groningen, The Netherlands Search for more papers by this author Jelle Slager Jelle Slager orcid.org/0000-0002-8226-4303 Molecular Genetics Group, Groningen Biomolecular Sciences and Biotechnology Institute, Centre for Synthetic Biology, University of Groningen, Groningen, The Netherlands Search for more papers by this author Sebastiaan P van Kessel Sebastiaan P van Kessel Molecular Genetics Group, Groningen Biomolecular Sciences and Biotechnology Institute, Centre for Synthetic Biology, University of Groningen, Groningen, The Netherlands Search for more papers by this author Kèvin Knoops Kèvin Knoops Molecular Cell Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Robin A Sorg Robin A Sorg Molecular Genetics Group, Groningen Biomolecular Sciences and Biotechnology Institute, Centre for Synthetic Biology, University of Groningen, Groningen, The Netherlands Search for more papers by this author Jing-Ren Zhang Jing-Ren Zhang orcid.org/0000-0003-4973-4243 Center for Infectious Disease Research, School of Medicine, Tsinghua University, Beijing, China Search for more papers by this author Jan-Willem Veening Corresponding Author Jan-Willem Veening [email protected] orcid.org/0000-0002-3162-6634 Molecular Genetics Group, Groningen Biomolecular Sciences and Biotechnology Institute, Centre for Synthetic Biology, University of Groningen, Groningen, The Netherlands Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland Search for more papers by this author Xue Liu Xue Liu orcid.org/0000-0001-6485-1865 Molecular Genetics Group, Groningen Biomolecular Sciences and Biotechnology Institute, Centre for Synthetic Biology, University of Groningen, Groningen, The Netherlands Center for Infectious Disease Research, School of Medicine, Tsinghua University, Beijing, China Search for more papers by this author Clement Gallay Clement Gallay orcid.org/0000-0002-6296-8928 Molecular Genetics Group, Groningen Biomolecular Sciences and Biotechnology Institute, Centre for Synthetic Biology, University of Groningen, Groningen, The Netherlands Search for more papers by this author Morten Kjos Morten Kjos orcid.org/0000-0003-4448-9082 Molecular Genetics Group, Groningen Biomolecular Sciences and Biotechnology Institute, Centre for Synthetic Biology, University of Groningen, Groningen, The Netherlands Department of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway Search for more papers by this author Arnau Domenech Arnau Domenech orcid.org/0000-0002-0829-511X Molecular Genetics Group, Groningen Biomolecular Sciences and Biotechnology Institute, Centre for Synthetic Biology, University of Groningen, Groningen, The Netherlands Search for more papers by this author Jelle Slager Jelle Slager orcid.org/0000-0002-8226-4303 Molecular Genetics Group, Groningen Biomolecular Sciences and Biotechnology Institute, Centre for Synthetic Biology, University of Groningen, Groningen, The Netherlands Search for more papers by this author Sebastiaan P van Kessel Sebastiaan P van Kessel Molecular Genetics Group, Groningen Biomolecular Sciences and Biotechnology Institute, Centre for Synthetic Biology, University of Groningen, Groningen, The Netherlands Search for more papers by this author Kèvin Knoops Kèvin Knoops Molecular Cell Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Robin A Sorg Robin A Sorg Molecular Genetics Group, Groningen Biomolecular Sciences and Biotechnology Institute, Centre for Synthetic Biology, University of Groningen, Groningen, The Netherlands Search for more papers by this author Jing-Ren Zhang Jing-Ren Zhang orcid.org/0000-0003-4973-4243 Center for Infectious Disease Research, School of Medicine, Tsinghua University, Beijing, China Search for more papers by this author Jan-Willem Veening Corresponding Author Jan-Willem Veening [email protected] orcid.org/0000-0002-3162-6634 Molecular Genetics Group, Groningen Biomolecular Sciences and Biotechnology Institute, Centre for Synthetic Biology, University of Groningen, Groningen, The Netherlands Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland Search for more papers by this author Author Information Xue Liu1,2, Clement Gallay1, Morten Kjos1,3, Arnau Domenech1, Jelle Slager1, Sebastiaan P Kessel1, Kèvin Knoops4, Robin A Sorg1, Jing-Ren Zhang2 and Jan-Willem Veening *,1,5 1Molecular Genetics Group, Groningen Biomolecular Sciences and Biotechnology Institute, Centre for Synthetic Biology, University of Groningen, Groningen, The Netherlands 2Center for Infectious Disease Research, School of Medicine, Tsinghua University, Beijing, China 3Department of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway 4Molecular Cell Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands 5Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland *Corresponding author. Tel: +41 21 6925625; E-mail: [email protected] Molecular Systems Biology (2017)13:931https://doi.org/10.15252/msb.20167449 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Genome-wide screens have discovered a large set of essential genes in the opportunistic human pathogen Streptococcus pneumoniae. However, the functions of many essential genes are still unknown, hampering vaccine development and drug discovery. Based on results from transposon sequencing (Tn-seq), we refined the list of essential genes in S. pneumoniae serotype 2 strain D39. Next, we created a knockdown library targeting 348 potentially essential genes by CRISPR interference (CRISPRi) and show a growth phenotype for 254 of them (73%). Using high-content microscopy screening, we searched for essential genes of unknown function with clear phenotypes in cell morphology upon CRISPRi-based depletion. We show that SPD_1416 and SPD_1417 (renamed to MurT and GatD, respectively) are essential for peptidoglycan synthesis, and that SPD_1198 and SPD_1197 (renamed to TarP and TarQ, respectively) are responsible for the polymerization of teichoic acid (TA) precursors. This knowledge enabled us to reconstruct the unique pneumococcal TA biosynthetic pathway. CRISPRi was also employed to unravel the role of the essential Clp-proteolytic system in regulation of competence development, and we show that ClpX is the essential ATPase responsible for ClpP-dependent repression of competence. The CRISPRi library provides a valuable tool for characterization of pneumococcal genes and pathways and revealed several promising antibiotic targets. Synopsis A CRISPRi knockdown library targeting 348 potentially essential genes in Streptococcus pneumoniae strain D39, in combination with high-throughput phenotyping identifies new essential genes involved in cell wall synthesis and in competence regulation. A CRISPRi knockdown library was constructed targeting 348 potentially essential genes in Streptococcus pneumoniae strain D39, as determined by Tn-seq. 254 out of 348 targeted genes showed growth phenotypes, providing a useful platform for the functional identification of hypothetical genes. High-content microscopy allowed linking genotypes with phenotypes and identified TarP and TarQ as being involved in polymerization of teichoic acid precursors. The essential ATPase ClpX, together with ClpP was shown to regulate competence development. Introduction Streptococcus pneumoniae (pneumococcus) is a major cause of community-acquired pneumonia, meningitis, and acute otitis media and, despite the introduction of several vaccines, remains one of the leading bacterial causes of mortality worldwide (Prina et al, 2015). The main antibiotics used to treat pneumococcal infections belong to the beta-lactam class, such as amino-penicillins (amoxicillin, ampicillin) and cephalosporines (cefotaxime). These antibiotics target the penicillin binding proteins (PBPs), which are responsible for the synthesis of peptidoglycan (PG) that plays a role in the maintenance of cell integrity, cell division, and anchoring of surface proteins (Sham et al, 2012; Kocaoglu et al, 2015). The pneumococcal cell wall furthermore consists of teichoic acids (TA), which are anionic glycopolymers that are either anchored to the membrane (lipo TA) or covalently attached to PG (wall TA) and are essential for maintaining cell shape (Brown et al, 2013; Massidda et al, 2013). Unfortunately, resistance to most beta-lactam antibiotics remains alarmingly high. For example, penicillin non-susceptible pneumococcal strains colonizing the nasopharynx of children remain above 40% in the United States (Kaur et al, 2016), despite the effect of the pneumococcal conjugate vaccines. Furthermore, multidrug resistance in S. pneumoniae is prevalent and antibiotic resistance determinants and virulence factors can readily transfer between strains via competence-dependent horizontal gene transfer (Chewapreecha et al, 2014; Johnston et al, 2014; Kim et al, 2016). For these reasons, it is crucial to understand how competence is regulated and to identify and characterize new essential genes and pathways. Interestingly, not all proteins within the pneumococcal PG and TA biosynthesis pathways are known (Massidda et al, 2013), leaving room for discovery of new potential antibiotic targets. For instance, not all enzymes in the biosynthetic route to lipid II, the precursor of PG, are known and annotated in S. pneumoniae. The pneumococcal TA biosynthetic pathway is even more enigmatic, and it is unknown which genes code for the enzymes responsible for polymerizing TA precursors (Denapaite et al, 2012). Several studies using targeted gene knockout and depletion/overexpression techniques as well as transposon sequencing (Tn-seq) have aimed to identify the core pneumococcal genome (Thanassi et al, 2002; Song et al, 2005; van Opijnen et al, 2009; van Opijnen & Camilli, 2012; Zomer et al, 2012; Mobegi et al, 2014; Verhagen et al, 2014). These genome-wide studies revealed a core genome of around 400 genes essential for growth either in vitro or in vivo. Most of the essential pneumococcal genes can be assigned to a functional category on the basis of sequence homology or experimental evidence. However, per the most recent gene annotation of the commonly used S. pneumoniae strain D39 (NCBI, CP000410.1, updated on 31-JAN-2015), approximately one-third of the essential genes belong to the category of “function unknown” or “hypothetical” and it is likely that several unknown cell wall synthesis genes, such as the TA polymerase, are present within this category. To facilitate the high-throughput study of essential genes in S. pneumoniae on a genome-wide scale, we established CRISPRi (clustered regularly interspaced short palindromic repeats interference) for this organism. CRISPRi is based on expression of a nuclease-inactive Streptococcus pyogenes Cas9 (dCas9), which together with expression of a single-guide RNA (sgRNA) targets the gene of interest (Bikard et al, 2013; Qi et al, 2013; Peters et al, 2016). When targeting the non-template strand of a gene by complementary base-pairing of the sgRNA with the target DNA, the dCas9-sgRNA-DNA complex acts as a roadblock for RNA polymerase (RNAP) and thereby represses transcription of the target genes (Qi et al, 2013; Peters et al, 2016) (Fig 1A). Note that S. pneumoniae does not contain an endogenous CRISPR/Cas system, consistent with interference with natural transformation and thereby lateral gene transfer that is crucial for pneumococcal host adaptation (Bikard et al, 2012). Figure 1. An IPTG-inducible CRISPRi system for tunable repression of gene expression in S. pneumoniae dcas9 and sgRNA sequences were chromosomally integrated at two different loci, and expression was driven by an IPTG-inducible promoter (Plac) and a constitutive promoter (P3), respectively. With addition of IPTG, dCas9 is expressed and guided to the target site by constitutively expressed sgRNA. Binding of dCas9 to the 5′ end of the coding sequence of its target gene blocks transcription elongation. In the absence of IPTG, expression of dCas9 is tightly repressed, and transcription of the target gene can proceed smoothly. Genetic map of CRISPRi luc reporter strain XL28. To allow IPTG-inducible expression, the lacI gene, driven by the constitutive PF6 promoter, was inserted at the non-essential prsA locus; luc, encoding firefly luciferase, driven by the constitutive P3 promoter was inserted into the intergenic sequence between gene loci spd_0422 and spd_0423; dcas9 driven by the IPTG-inducible Plac promoter was inserted into the bgaA locus; sgRNA-luc driven by the constitutive P3 promoter was inserted into the CEP locus (between treR and amiF). The CRISPRi system was tested in the luc reporter strain XL28. Expression of dcas9 was induced by addition of different concentrations of IPTG. Cell density (OD595) and luciferase activity (shown as RLU/OD) of the bacterial cultures were measured every 10 min. The values represent averages of three replicates with SEM. RNA-Seq confirms the specificity of the CRISPRi system in S. pneumoniae. RNA sequencing was performed on the luc reporter strain XL28 (panel B) with or without 1 mM IPTG. The dcas9 and luc genes are highlighted. Data were analyzed with T-REx and plotted as a volcano plot. P-value equals 0.05 is represented by the horizontal dotted line. Two vertical dotted lines mark the twofold changes. Download figure Download PowerPoint Using Tn-seq and CRISPRi, we refined the list of genes that are either essential for viability or for fitness in S. pneumoniae strain D39 (Avery et al, 1944). To identify new genes involved in pneumococcal cell envelope homeostasis, we screened for essential genes of unknown function (as annotated in NCBI), with a clear morphological defect upon CRISPRi-based depletion. This identified SPD_1416 and SPD_1417 as essential peptidoglycan synthesis proteins (renamed to MurT and GatD, respectively) and SPD_1198 and SPD_1197 as essential proteins responsible for precursor polymerization in TA biosynthesis (hereafter called TarP and TarQ, respectively). Finally, we demonstrate the use of CRISPRi to unravel gene regulatory networks and show that ClpX is the ATPase subunit that acts together with the ClpP protease as a repressor for competence development. Results Identification of potentially essential genes in S. pneumoniae strain D39 While several previous studies have identified many pneumococcal genes that are likely to be essential, the precise contribution to pneumococcal biology has remained to be defined for most of these genes. Here, we aim to characterize the functions of these proteins in the commonly used S. pneumoniae serotype 2 strain D39 by the CRISPRi approach. Therefore, we performed Tn-seq on S. pneumoniae D39 grown in C+Y medium at 37°C, our standard laboratory condition (see 4). We included all genes that we found to be essential in our Tn-seq study, and added extra genes that were found to be essential by previous Tn-seq studies with a serotype 4 strain TIGR4 (van Opijnen et al, 2009; van Opijnen & Camilli, 2012) in the CRISPRi library (see below). Finally, 391 potentially essential genes were selected, and the genes are listed in Dataset EV1. CRISPRi enables tunable repression of gene transcription in S. pneumoniae To develop the CRISPR interference system, we first engineered the commonly used LacI-based isopropyl β-D-1-thiogalactopyranoside (IPTG)-inducible system for S. pneumoniae (see 4). The dcas9 gene was placed under control of this new IPTG-inducible promoter, named Plac, and was integrated into the chromosome via double crossover (Fig 1A and B). To confirm the reliability of the CRISPRi system, we tested it in a reporter strain expressing firefly luciferase (luc), in which an sgRNA targeting luc was placed under the constitutive P3 promoter (Sorg et al, 2015) and integrated at a non-essential locus (Fig 1B). To obtain high efficiency of transcriptional repression, we used the optimized sgRNA sequence as reported previously (Chen et al, 2013) (Fig EV1A). Click here to expand this figure. Figure EV1. Properties of the designed IPTG-inducible CRISPRi system. Related to Fig 1 Secondary structure of the complex of sgRNAluc binding to the 5′ end encoding sequence of the luc gene. Genetic map of CRISPRi luc reporter strain XL29. Genetic map of strain XL28 is shown in Fig 1B. Strain XL29 is genetically identical to XL28 but lacking the sgRNAluc. The CRISPRi system is tightly controlled by IPTG. Luminescence and OD595 were measured every 10 min, and averages of three replicates with SEM were used for plotting. Note that, in XL29, induction of dCas9 without sgRNAluc did not influence growth or luc expression. In XL28, without addition of IPTG, no repression on luc was observed compared with XL29. Download figure Download PowerPoint Induction of dCas9 with 1 mM IPTG resulted in quick reduction in luciferase activity; ~30-fold repression of luciferase expression was obtained within 2 h without substantial impact on bacterial growth (Fig 1C). Furthermore, the level of repression was tunable by using different concentrations of IPTG (Fig 1C). To test the precision of CRISPRi in S. pneumoniae, we determined the transcriptome of the sgRNAluc strain (strain XL28) by RNA-Seq in the presence or absence of IPTG. The data were analyzed using Rockhopper (McClure et al, 2013) and T-REx (de Jong et al, 2015). The RNA-Seq data showed that the expression of dCas9 was stringently repressed by LacI without IPTG and was upregulated ~600-fold upon addition of 1 mM IPTG after 2.5 h. Upon dCas9 induction, the luc gene was significantly repressed (~84-fold) (Fig 1D). Our RNA-Seq data showed that the genes (spd_0424, spd_0425, lacE-1, lacG-1, lacF-1) that are downstream of luc, which was driven by a strong constitutive promoter without terminator, were significantly repressed as well (Appendix Fig S1A). This confirms the reported polar effect of CRISPRi (Qi et al, 2013). In addition, induction of dCas9 in the sgRNA-deficient strain XL29 (Fig EV1B) led to no repression of the target gene (Fig EV1C). By comparing strains with or without sgRNAluc, we found that repression in our CRISPRi system is stringently dependent on the expression of both dCas9 and the sgRNA, and detected no basal level repression (Fig EV1C). Furthermore, we compared the transcriptome of luc reporter strains with sgRNAluc (strain XL28) and without sgRNAluc (strain XL29) both grown in the presence of 1 mM IPTG. This showed that galT-2, galK, and galR were upregulated in both strains, indicating that these genes are activated in response to the inducer IPTG and not by the CRISPRi system itself (Dataset EV2). We also noted a slight repression of several competence genes in both XL28 and XL29 with 1 mM IPTG (Dataset EV2). Since this repression does not rely on the presence of a functional CRISPRi system, we anticipate that these changes are due to the noisy character of the competence system (Aprianto et al, 2016; Prudhomme et al, 2016). Taken together, the IPTG-inducible CRISPRi system is highly specific. Construction and growth analysis of the CRISPRi library We next used the CRISPRi system to construct an expression knockdown library of pneumococcal essential genes. An sgRNA to each of the 391 potentially essential genes was designed as described previously (Larson et al, 2013) (Dataset EV3). Based on the sgRNAluc plasmid (Fig 2A), we tested two different cloning strategies to introduce the unique 20-nt base-pairing region for each gene: infusion cloning and inverse PCR (Ochman et al, 1988; Irwin et al, 2012; Larson et al, 2013) (Fig EV2A). For infusion cloning, we synthesized two complementary primers consisting of the 20-nt base-pairing region flanked by 15-nt overlap sequences. The two complementary primers were then annealed to form a duplex DNA fragment and cloned into the vector by the infusion reaction, followed by direct transformation into S. pneumoniae D39 strain DCI23. With inverse PCR, we used a phosphorylated universal primer, together with a gene-specific primer to fuse the 20-nt base-pairing region into the vector by PCR, followed by blunt-end ligation and direct transformation into S. pneumoniae D39 strain DCI23. We compared the efficiency of the two methods by creating sgRNA strains targeting the known essential gene folA (spd_1401). Depletion of folA causes a clear growth defect, which could thus be used to test the functionality of sgRNAfolA in transformants. We found that 79% of the transformants produced by infusion cloning had a growth defect upon dCas9 induction with IPTG (38 out of 48 colonies), whereas 26% of the transformants generated by inverse PCR showed a phenotype (12/46). Sequencing validated that transformants with a growth defect contained the correct sgRNA sequence. Considering the convenience and efficiency, we adopted the infusion cloning strategy for sgRNA cloning in this study. All sgRNA constructs were sequence verified, and we considered them genetically functional when the sgRNA did not contain more than 1 mismatch to the designed sgRNA and no mismatches in the first 14-nt prior to the PAM. Using this approach, after a single round of cloning and sequencing, we successfully constructed 348 unique sgRNA strains (see 4). Note that we are still in the process of constructing the remaining 43 sgRNA strains, the failure of which is likely caused by technical reasons (e.g., incorrect oligonucleotides, poor oligo annealing, low transformation). Figure 2. Construction and growth analysis of the CRISPRi library A. The plasmid map of the sgRNA cloning vector (pPEPX-P3-sgRNAluc). The sgRNA expression vector is a S. pneumoniae integration vector. It contains a constitutive P3 promoter, a spectinomycin-selectable marker (SpR), two homology sequences (ISU and ISD) for double crossover integration at the CEP locus (Sorg et al, 2015), and the sgRNA sequence. The sgRNA chimera contains a base-pairing region (blue), dCas9-binding handle (red), and the S. pyogenes transcription terminator (purple). B, C. Growth analysis of the whole library. (B) Classification of the 348 genes targeted by the CRISPRi library according to growth analysis: A represents the 24 strains that only showed increased autolysis; B represents the 24 strains showing both increased autolysis and growth defects; C represents the 206 strains that showed only growth defects; D represents the 94 strains with no phenotype. Criteria for determination of a growth defect and increased lysis are demonstrated in Fig EV2B–E. (C) Comparison of the OD595 of IPTG-induced cells (ODIPTG) to the OD595 of uninduced cells (ODuninduced) at a time point. The time point at which uninduced cells have an optical density (595 nm) closest to 0.1 was selected for the plotting. y-axis represents the value of ODIPTG divided by ODuninduced. The red data points in the dark orange area (174/348 strains) correspond to strains displaying a strong growth defect (more than fourfold); points in the light orange area demonstrate a moderate growth defect of twofold to fourfold (71/348 strains). The same type of analysis was performed on 36 negative control strains, shown as the black data points. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Schematic of the inverse PCR cloning strategy and growth analysis of the CRISPRi library. Related to Fig 2 A. Schematic of the infusion cloning and comparison with inverse PCR. For the infusion method used for sgRNA cloning, two gene-specific primers were designed for each cloning. Primer 1 and primer 2 are complementary, and they contain 15-bp homology sequences to the adjoining region, flanking the 20-bp base-pairing region of the sgRNA encoding sequence of the vector. For inverse PCR, a 20-nt new base-pairing sequence was included in the forward gene-specific primer. The universal reverse primer was phosphorylated, to allow circularization of the vector by ligation after amplification. B–E. Definition of the growth phenotype classification utilized in Fig 2B. The data points used in Fig 2C are indicated by double-headed arrows. (B) Classification as OD-difference phenotype (growth defect), exemplified by the fusA knockdown dataset. Growth curves of IPTG-induced and uninduced cells are shown in the top graph. In the bottom graph, the difference in log2(OD595) between IPTG-induced and uninduced cells is plotted for each time point. Datasets that contain points in the shaded area (i.e., having a > fourfold difference) are classified as having a significant growth defect. (C) Classification of the increased-lysis phenotype exemplified by the yabA knockdown dataset. Growth curves of IPTG-induced and uninduced cells are shown in the top graph. A best-fit straight line is created using the last 10 data points of the IPTG-treated cells (i.e., last 90 min). If the slope of this line is more negative than 0.05 h−1, the strain is classified as having an increased-lysis phenotype. Additionally, datasets were normalized to the maximum OD595 reached, plotted in the bottom graph. When the normalized values of the IPTG-induced cells fall below 0.7 (i.e., 70% of ODmax), the strain is also classified as having an increased-lysis phenotype (shaded area). The black arrow points to the normalized dataset of bacterial growth in IPTG free medium, while the red arrow points to the normalized dataset of bacterial growth in medium with 1 mM IPTG. Note that while the example used (yabA) fulfills both criterions, fulfilling one of them is sufficient. Panels (D and E) show examples of “growth defect and increased lysis” and “No phenotype”, respectively, based on the criteria demonstrated in panels (B and C). Download figure Download PowerPoint To examine the effects of CRISPRi-based gene silencing, growth was assayed both in the presence and absence of 1 mM IPTG for 18 h in real time by microtiter plate assays. Two types of growth phenotypes were defined and identified: a growth defect and increased lysis (Fig EV2B–E). As shown in Fig 2B, CRISPRi-based repression of transcription led to a growth defect in 230 genes, 48 genes showed increased lysis, including 24 that demonstrated both a growth defect and increased lysis, and 94 genes showed no defect (see Dataset EV1). In total, 254 out of 348 target genes (about 73%) repressed by CRISPRi showed growth phenotypes. Comparing the optical densities between the uninduced and induced cells at the time point at which uninduced cells reached an OD595 of ~0.1, 174 genes repressed by CRISPRi displayed a more than fourfold growth defect, and 254 genes showed a more than twofold growth defect (Fig 2C). To further validate the specificity of the CRISPRi system, CRISPRi strains targeting eight genes identified as essential and eight genes as dispensable by Tn-seq were included in the growth analysis. The selected dispensable genes are present as a monocistron or are in an operon with other non-essential genes. As shown in Fig EV3A, no apparent growth defects could be observed when these non-essential genes were targeted by CRISPRi, while repression of essential genes led to strong growth defects (Fig EV3B). Click here to expand this figure. Figure EV3. Growth of CRISPRi strains targeting dispensable or essential genes Growth curves of eight CRISPRi strains targeting dispensable genes. Growths of CRISPRi strains were performed in C+Y medium with (red lines) or without (cyan lines) 1 mM IPTG. Growth curves of eight CRISPRi strains targeting essential genes, performed similarly to panel (A). Download figure Download PowerPoint It should be noted that CRISPRi repression of dispensable genes that are cotranscribed with essential genes can lead to growth phenotypes (Appendix Fig S1), which is d
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