Identification of two novel highly inducible promoters from Bacillus licheniformis by screening transcriptomic data

Journal Pre-proof Identification of two novel highly inducible promoters from Bacillus licheniformis by screening transcriptomic data

Feiyan Yuan, Kang Li, Cuixia Zhou, Huiying Zhou, Huan Liu, Haonan Chai, Fuping Lu, Huitu Zhang PII:

S0888-7543(19)30097-7

DOI:

https://doi.org/10.1016/j.ygeno.2019.10.021

Reference:

YGENO 9390

To appear in:

Genomics

Received date:

24 February 2019

Revised date:

31 July 2019

Accepted date:

29 October 2019

Please cite this article as: F. Yuan, K. Li, C. Zhou, et al., Identification of two novel highly inducible promoters from Bacillus licheniformis by screening transcriptomic data, Genomics (2018), https://doi.org/10.1016/j.ygeno.2019.10.021

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2018 Published by Elsevier.

Journal Pre-proof

Identification of two novel highly inducible promoters from Bacillus licheniformis by screening transcriptomic data

Feiyan Yuan, Kang Li, Cuixia Zhou, Huiying Zhou, Huan Liu, Haonan Chai, Fuping Lu*, Huitu Zhang* Author affiliations: Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education,

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College of Bioengineering, Tianjin University of Science & Technology, Tianjin 300457, PR China

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*Corresponding authors: Huitu Zhang and Fuping Lu

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Mailing address: Industrial Microbiology Laboratory, College of Biotechnology, Tianjin University of Science & Technology, No. 29, 13 Main Street, Tianjin

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Economic and Technological Development Zone, Tianjin 300457, PR China Phone: +86-22-60600160

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[email protected]

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e-mail: [email protected]

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Fax: +86-22-60600810

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Highlights :  We sequenced the transcriptome of B. licheniformis TCCC11148.  Significantly differentially expressed genes, TSS, TTS, and operons were predicted.  The candidate inducible promoters p707 and p1004 exhibited the strongest activity.

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Abstract: Bacillus licheniformis TCCC11148 is an important industrial strain used to produce alkaline protease. In this study, the transcriptome of B. licheniformis TCCC11148 was analyzed by high throughput RNA sequencing (RNA-Seq) to identify genes that are expressed differentially in the different phases were detected using RNA-Seq. In total, 440 differentially expressed genes between the 12 h and 48 h groups were identified, including 267 up- and 173 downregulated genes. Additionally, 198 differentially expressed genes were identified in the 48 h vs. the 60 h group, including 182 up- and 16 downregulated genes. To screen for novel inducible promoters, an alkaline protease reporter gene was used to test 24 promoters from 66 candidate genes with obviously higher expression levels (RPKM values) than the control group based on the transcriptome data of B. licheniformis in different phases. Gene 707, related to coenzyme transport and metabolism, and gene 1004, related to posttranslational modification were identified as likely having inducible promoters. The expression level of recombinant strains with reporter genes under the control of promoters p707 and p1004 were 8 times higher than that of the control group. This study contributes a method for finding useful inducible promoters for industrial use based on transcriptomic data.

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Keywords: Bacillus licheniformis; RNA-Seq; inducible promoter; transcriptional regulation

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1. Introduction Bacillus licheniformis is a common Gram-positive thermophilic bacterium that can be isolated from many different regions of the soil [1]. Moreover, it is widely used in agricultural and industrial production [2]. For example, it can be used as a microbial fertilizer [1], and also produces enzymes [3] and antibiotics [4]. It is widely used in large-scale industrial production because it can secrete a large amount of protein [5]. Proteases are the most important industrial enzymes accounting for about 50% of the total industrial enzyme market [6]. They are widely used in detergents, food processing, medicine, leather, silk and other industries [7], [8]. Especially the alkaline serine protease produced by B. licheniformis, which is the main enzymatic additive in many detergents, has important application value. Their annual output is estimated at about 500 metric tons of pure enzyme protein [9]. B. licheniformis TCCC11148 is an important industrial strain with high alkaline protease production, and strains with higher enzyme production and commercial value were obtained through genetic modification. In recent years, RNA sequencing (RNA-Seq) has been developed as an effective method for studying large-scale transcriptome patterns, which improved the efficiency

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and speed of gene discovery [10]. Next generation sequencing technology, which is widely used in plant chemistry and natural medicine [11], is a powerful tool for transcriptome assembly and annotation. The gene expression profiles of important industrial or medicinal plants in specific developmental stages or tissues can be rapidly obtained by high-throughput transcriptome sequencing [12]. At the same time, the technology can provide insights into metabolic processes, thus laying a foundation for increasing the biosynthesis of target metabolites [13] [14]. Transcriptional analysis based on RNA-Seq is one of the most powerful tools. It not only provides important insights into the functional elements of the genome, gene expression patterns and regulation [15], but also provides a simpler, more economical and effective method for application research [16]. To date RNA-Seq has been applied in many Bacillus species, such as Bacillus subtilis [17], Bacillus licheniformis [18] and Bacillus thuringiensis [19]. Transcriptome analysis was performed on the growth phase of B. licheniformis TCCC11148 at different times using RNA-Seq technology. The results indicate that the gene expression or metabolic pathway of B. licheniformis has changed at different stages of growth, which will be helpful for understanding the physiology and phenotypes of important B. licheniformis strains. A promoter is the switch that controls the expression of a target gene, and inducible promoters have unique advantages because they can be activated using specific inducers according to different needs. However, the induction mechanism can even be activated spontaneously without adding any inducer. The mining of regulatory elements by combining high throughput screening and RNA-Seq techniques has been proven to be an effective novel strategy. Wang et al. identified 1203 active promoter candidates in B. thuringiensis based on transcriptome data [20]. Liao et al. screened strong promoters based on RNA-Seq data of Bacillus amyloliquefaciens XH7 [21]. Liu et al. found a strong promoter, pBL9, which can be used to highly express protein in the host B. subtilis, based on an analysis of the transcriptome of B. licheniformis [22]. Nevertheless, the number of promoters that can be used in industry is limited, and there are many problems in startup ability and regulation mode. In order to obtain more promoters with high transcriptional intensity and convenient induction mechanisms, more in-depth studies are needed. In an earlier project, we sequenced, de- novo assembled and annotated the genome of B. licheniformis TCCC11148. In this study, three growth stages of the transcriptome were selected for sequencing. We investigated transcription changes in B. licheniformis TCCC11148 and showed that hundreds of genes were up- and downregulated in different growth phases. Finally, we used this differential transcriptome of B. licheniformis to screen for highly inducible promoters.

2. Materials and methods 2.1 Strains and plasmids The details of the strains and plasmids used in this study are listed in Supplementary Table 1. B. licheniformis TCCC11148 was collected by our laboratory. TCCC11148 is an alkaline protease-producing strain obtained by traditional

Journal Pre-proof mutagenesis and selection. The genome sequence of TCCC11148 was deposited in GenBank under the accession number CP033218 (Yuan et al., 2018). B. licheniformis ΔfimV-TCCC11148 is a knockout strain related to alkaline protease production. The reporter gene, alk was cloned from Bacillus clausii strain TCCC11004 (GenBank Sequence ID: FJ940727.1; Our laboratory). E. coli EC135 [23] and E. coli EC135/pM.Bam [23] were obtained from Nankai University.

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2.2 Bacterial culture conditions B. licheniformis TCCC11148 was grown in 50 mL Luria-Bertani (LB) medium (1% tryptone (OXOID, UK), 0.5% yeast extract (OXOID, UK), and 1% NaCl) in a 250 mL flask at 37℃ with shaking at 220 rpm for 12 h. The following concentrations of antibiotics were used for selection: 100 μg/mL ampicillin (Amp), 50 μg/mL Spectinomycin (Spc) and 20 μg/mL tetracycline (Tet).

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2.3 RNA isolation, library construction, and sequencing B. licheniformis TCCC11148 was activated from −80°C glycerol stocks and grown on Luria-Bertani (LB) solid plates at 37°C for 12–14 h, after which a single colony was used to inoculate 50 mL of seed medium in a 250 mL flask and grown at 37°C and 220 rpm for 12 h. Then, 2 mL of the resulting seed culture was transferred into 100 mL of fermentation medium containing 6.4% maizena corn starch, 4% soybean meal, 0.4% Na2 HPO 4 , and 0.03% KH2 PO4, and incubated at 37°C and 220 rpm. Samples of the fermentation broth were taken for RNA extraction at 12, 48 and 60 h.

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Total RNA of each sample was extracted using TRIzol Reagent (Invitrogen, USA) and the RNeasy Mini Kit (Qiagen, Germany). Total RNA of each sample was quantified and qualified using a 2100 Bioanalyzer (Agilent, USA), NanoDrop (Thermo Fisher Scientific, USA) and 1% agarose gel electrophoresis. The rRNA was depleted from total RNA using the Ribo-Zero rRNA Removal Kit (Illumina). The rRNA-free mRNA was then fragmented and reverse-transcribed. First strand cDNA was synthesized using ProtoScript II Reverse Transcriptase (NEB, USA) with random primers and Actinomycin D (Thermo Fisher Scientific, USA). The second-strand cDNA was synthesized using the Second Strand Synthesis Enzyme Mix (including dACGTP/ dUTP; NEB, USA). The double-stranded

cDNA was purified using the AxyPrep Mag PCR Clean-up kit (Axygen, USA) and then treated with End Prep Enzyme Mix (NEB, USA) to repair both ends and add a dA-tail in one reaction, followed by T-A ligation to add adaptors to both ends. Size selection of adaptor- ligated DNA was then performed using the AxyPrep Mag PCR Clean- up kit, and the cDNA fragments were purified and amplified by PCR to produce the final cDNA library. Libraries with different indices were multiplexed and loaded onto an Illumina HiSeq instrument according to manufacturer ’s instructions. Sequencing was carried out using a 2×150 paired-end configuration; image analysis and base calling were conducted by the HiSeq Control Software (HCS) + OLB + GAPipeline-1.6 (Illumina, USA) on the HiSeq instrument. In order to remove technical sequences, including adapters, polymerase chain reaction (PCR) primers, or fragments thereof, and bases

Journal Pre-proof with a quality lower than 20, the filter pass data in fastq format were processed using Cutadapt (version 1.9.1) [24]. The sequences were processed by GENEWIZ (Jiangsu, China). These raw sequencing data have been deposited in the Sequence Read Archive (SRA) (http://www.ncbi.nlm.nih.gov/sra/) at the National Center for Biotechnology Information (NCBI) under the accession number SRP178635.

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2.4 RNA-sequencing data analysis The obtained clean reads were mapped onto the reference genome of B. licheniformis TCCC11148 (NCBI database; CP_033218). Gene expression was normalized using the reads per kilobase per million method (RPKM) [25]. All genes returned from both strains were searched against the gene ontology (GO) database (http://www.geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes (KEEG) database (http://en.wikipedia.org/wiki/KEGG). Rockhopper [26] was used to predict the location of operons as well as the transcription initiation- and termination sites. Furthermore RBS-finder was used to predict SD sequences. Softberry (http://www.softberry.com/berry.phtml?topic=bprom&group=programs&subgroup=gf indb) was used to predict bacterial promoters immediately upstream of the open reading frame. The DBTBS database (http://dbtbs.hgc.jp/) was used to analyze putative binding sites of σ-factors and transcription factors (TFs).

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2.5 Construction of plasmids The recombinant plasmids and primers used in this study are listed in supplementary tables 1 and 2, respectively. Firstly, the primer pair alk-F/R, was used to amplify the reporter gene using B. clausii TCCC11104 chromosomal DNA as template. The amplified fragment had BamHI and SphI sites 5' and 3', respectively. The fragment obtained by PCR was digested and ligated between the same restriction sites of the plasmid pWH1520, and ligation product was used to transform E. coli EC135, yielding the plasmid alk-pWH1520. Secondly, the promoter-5′ UTR DNA region complex of 24 promoters was amplified from the genomic DNA of B. licheniformis TCCC11148 using the primer pairs listed in supplementary table 2, which contained SpeI and KpnI or SpeI and BamHI sites at the 5′ and 3′ ends, respectively. The PCR-amplified fragments were digested and ligated between the correspondent sites of alk-pWH1520 to construct the expression plasmid library palk-pWH520. 2.6 Transformation of B. licheniformis ΔfimV-TCCC11148 Plasmid DNA was extracted from E. coli EC135 and transferred into E. coli EC135 and E. coli EC135/pM.Bam. Then, the plasmid DNA was extracted from E. coli EC135/pM.Bam and transferred into B. licheniformis ΔfimV-TCCC11148 through electroporation [27]. The cells were exposed to a single electrical pulse using a field strength of 12.5 KV/cm for 4.5-6 ms. 2.7 Enzyme activity assay The fermentation supernatants were analyzed for enzyme activity of alkaline

Journal Pre-proof protease using the Folin-Phenol method [28]. One unit of enzyme activity was defined as the amount of enzyme required to produce 1 μg tyrosine from casein in 25 mM boracic acid buffer (pH 10.5) at 40°C. The reactions were conducted in volumes of 1mL, started by adding enzyme solution and terminated by 0.4 M trichloroacetic acid. The data represent the averages of three independent experiments and the error bars represent the corresponding standard errors of the mean.

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2.8 Statistical analysis The experimental data were analyzed statistically by GraphPad Prism 5 software. And P < 0.05 was considered statistically significant. Three biological repeats per sample. Two comparisons were made between different groups. The values obtained in the experiments was expressed as mean ± standard deviation (SD). The data are analyzed by Variance analysis and Student’s t test where necessary. And the error line wad expressed as SD.

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3. Results 3.1 Summary of RNA-Seq data According to the extracellular protease activity (Fig. 1) of strain TCCC11148, the amount of enzyme produced reached its peak at 48 h and then declined at 60 h. Variance analysis showed that there was significant difference in enzyme activity at different time of fermentation. The analysis showed that the corresponding results of 48 and 60 h compared with 12 h were p < 0.01 and p < 0.05, respectively. Notably, almost no enzyme was produced at 12 h. In order to compare the changes of the transcriptome at time points with obvious characteristics of enzyme activity expression, we chose 12 h, 48 h and 60 h as the time points for RNA sample preparation. Three parallel RNA samples were made at each time point for Illumina sequence analysis. To obtain a global transcriptome view of the B. licheniformis TCCC11148, the isolated mRNAs was subjected to high-throughput Illumina sequencing after removal of the ribosomal RNAs. We obtained a total of 36.45, 38.71 and 40.08 million clean reads from the samples taken at 12, 48 and 60 h, respectively, with an average length of 147 bp. In total, 34.68 (95.2%), 37.57 (97.1%) and 38.70 (96.7%) million reads were uniquely mapped to the genome regions. Based on the RPKM values, we quantified the expression of 5166 genes in the B. licheniformis TCCC11148 genome database (Supplementary Table 3). The differentially expressed genes (DEGs) were extracted using the DESeq2 software using a p value < 0.05 and log2 (fold-change) > 1 as the criteria. Consequently, 440, 647 and 198 DEGs were identified between the three different time points, respectively. The numbers of DEGs among the various time points were analyzed using a cylindrical figure and a Venn diagram (Fig. 2). DEGs were subjected to GO functional enrichment analysis. Figure 3 shows the subsystems with the numbers of the involved DEGs. The most abundant subsystems of the DEGs were related to “catalytic activity”, followed by “binding” and “metabolic process”. In addition, there were also large numbers of DEGs involved in

Journal Pre-proof “cell part” and “transporter activity”. Simultaneously, the main biochemical metabolic pathways and signal transduction pathways related to the differentially expressed genes were identified by pathway enrichment. Supplementary table 4 shows all the pathways with the numbers of the involved DEGs. The most abundant pathways of the DEGs were related to “biosynthesis of amino acids” in the 12 h vs. 48 h group, followed by “carbon metabolism”, while “ABC transporters” had the most DEGs in the 12 h vs. 60 h group, followed by “biosynthesis of amino acids”, which suggested that there were significant changes in primary metabolism between the various growth phases, as expected. In addition, there were also many DEGs involved in “citrate cycle and ribosome” and “quorum sensing”, indicating that the physiology or phenotype also changed significantly between the different phases.

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3.2 Prediction of transcription initiation sites (TSS), transcription te rmination sites (TTS) and operons Prediction of operons, transcriptional initiation sites and transcriptional termination sites was conducted using Rockhopper. A total of 2500 transcriptional initiation sites (TSS) and 2507 transcriptional termination sites (TTS) were predicted by sequence analysis (Supplementary Table 5). Of all the 1183 operons, 11 contained more than 10 cistrons, and the longest operon was predicted to contain 17 (1_1289 to 1_1305). By contrast, 54% had only two genes associated with the operon (Supplementary Table 6). This information will help the genetic manipulation of the strain in the future.

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3.3 Analysis of candidate inducible promoters We screened 24 candidate genes to identify inducible promoters. The RPKM values of these candidate genes (Fig. 4) showed a significant change trend across the three different sampling time-points. According to the changes of the RPKM values, we expected that the promoters of these candidate genes was more likely to be inducible. We predicted the −35, and −10 elements and the lengths of the spacers using Softberry software. Additionally, we predicted 18 sigma factor binding sites within the candidate promoters using the DBTBS database (Supplementary Table 7). Among these, three were for sigma A, B and F, while two were for sigma D and E. Furthermore, only eight promoters were predicted to be controlled by only one sigma- factor. Five promoters may be controlled by multiple sigma- factors, and the other promoters were not predicted to interact with specific sigma factors. In addition, 21 different TF-binding sites were predicted in these promoters. 3.4 Screening of efficient inducible promoters screening based on RNA-Seq data The 24 alk expression plasmids were introduced into the knockout strain B. licheniformis ΔfimV-TCCC11148. At the same time, the plasmid alk-pWH1520 with alk reporter gene but no promoter was used as a control group. The selected inducible promoter candidates were used to activate transcription under the aforementioned culture conditions, and the highest expression of enzyme activity was detected. According to the results (Fig. 5), p707 and p1004 showed the best performance (1541

Journal Pre-proof ± 11 U/mL and 1578 ± 42 U/mL, respectively) among all promoters and were approximately eight-fold stronger than the control group (196 ± 4 U/mL). Furthermore, p1329 showed higher expression (490 ± 16 U/mL) than other promoters. The remaining promoters were almost identical to the control group and showed no significant expression activity. These results illustrated that p707 and p1004 have the potential to be developed into strong inducible promoters.

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4. Discussion In this study, we determined the transcriptome profiles of B. licheniformis across the various growth phases using an RNA-Seq approach. The two highly active inducible promoters p707 and p1004 were screened from 24 candidate promoters. If they have a high gene expression level under favorable, adverse or threatening conditions, they may contain strong promoters or promoters that may be reactive to certain environmental signals [29]. Naturally, large-scale information about the changes of the strain’s transcriptome can help identify useful endogenous promoters [30]. Therefore, with the aim of isolating effective inducible promoters, we determined the changes in gene expression by comparing and analyzing the transcriptional data of the culture of B. licheniformis TCCC 11148 in different periods. The transcriptional structure of bacteria is vital for understanding prokaryotic gene regulation. By predicting the secondary structure, we found a palindrome between the initiation codon of the alk gene and the promoters p953, p1303, p1309, p1442, p1584, p1613, p1709, p1827, p1859, and p4107. This structure is clearly incompatible with highly active promoters. We suspected that the promoter's inability to express the target gene may be related to this type of sequence-specific transcriptional obstruction. In earlier studies, Liu et al. [31] used β-galactosidase as the report gene for four promoters which were successfully screened based on the transcriptional data of B. subtilis, and the activity of the four promoters was stronger than that of the widely used strong promoter p43. Dmitri et al. [32] showed that RNA-Seq is a powerful tool for whole-transcriptome analysis and that the method can help identify promoter-associated elements. Bacterial promoters are principally recognition by a sigma factor, which binds to the RNA polymerase and initiates transcription [33]. Transcription factors further recognize unique DNA elements with sequence specificity to activate transcription [34]. Promoters p707 and p1004 not only have binding sites for specific sigma factors, but also many other putative factors involved in transcription regulation (Supplementary Table 5). RocR is defined as transcriptional activator; Transcription factor ComA is involved in late growth expression and is a positive regulator of genes that respond to environmental stress [35]; DegU is involved in various post-exponential phase responses [36]; Gene 707 is located in an operon, and we speculated that multiple transcriptional factors in the promoter region coordinate with sigma factors, so that the target gene can be highly expressed when needed. In conclusion, we used RNA-Seq to characterize the transcriptomic changes of B. licheniformis during different culture periods, and thereby identified novel inducible

Journal Pre-proof promoters. Our data provide a deeper understanding of the transcriptional network and genetic structure of B. licheniformis. These results greatly promote our understanding of different gene regulation mechanisms, and the identified promoter elements provide new ways to predict the expression levels of transcripts in the future, which will help us to find more types of promoters that may be useful for industrial applications.

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Acknowledgments This research was supported by the National Natural Science Foundation of China (Grant number: 81373309), the National High Technology Research and Development Program of China (Grant number: 2013AA102803; Task number: 2013AA102803C) and the National Key Research and Development Program of China (Grant number: 2017YFB0308401). The authors declare no competing financial interest.

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Supporting information Supplementary Figure 1—Construction flowchart of p-alk-pWH1520. Supplementary Table 1—Detailed information on the strains and plasmids used in this study. Supplementary Table 2—All primers used in this study. Supplementary Table 3—Gene expression level table. Supplementary Table 4—All pathways with the numbers of the involved DEGs. Supplementary Table 5—TSS and TTS prediction results. Supplementary Table 6—Operator prediction results. Supplementary Table 7—Analysis of the basic components of candidate promoters.

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Fig. 3 The number of differentially expressed genes in the differential gene GO histogram and the GO terms. (a) Group 12h vs 48h. (b) Group 12h vs 60h. (c) Group 48h vs 60h. The

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Fig. 4 Cluster map of candidate differentially expressed genes (please refer to the online edition of this paper for colors).

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