The search and analysis of a CRISPR-Cas system in Escherichia coli HS with subsequent scanning for the corresponding phage races based on the spacers of the detected cripsr array using bioinformatic methods

Ivanova E.I.; Dzhioev Yu. P.; Borisenko A. Yu.; Peretolchina N.P.; Stepanenko L.A.; Paramonov A.I.; Grigorova E.V.; Nemchenko U.M.; Tunik T.V.; Kungurtseva E.A.

the search and analysis of a crispr-cas system in ESCHERICHIA COLIhs with subsequent scanning for the corresponding phage races based on the spacers of the detected cripsr array using bioinformatic methods

Ivanova Б!1И, Dzhioev YuP2, Borisenko AYu2, Peretolchina NP2, Stepanenko LA2, Paramonov AI1, Grigorova EV1, Nemchenko UM1, Tunik TV1, Kungurtseva EA1

1 Scientific Center for Family Health and Human Reproduction Problems, Irkutsk

2 Research Institute for Biomedical Technologies of Irkutsk State Medical University, Irkutsk

CRISPR-Cas is an immune system of prokaryotes that protects them against alien replicons, mainly viruses and plasmids. Short sequences (spacers) complementary to the regions of a viral or plasmid genome are inserted into a CRISPR array conferring resistance to reinfection. Infections caused by Escherichia coli still present a serious challenge for clinical medicine. The aim of this study was to scan the genome of Escherichia coli HS for CRISPR-Cas components. The search was conducted using MacSyFinder (Macromolecular System Finder, ver. 1.0.2.), a program for bioinformatic modelling. Sequence homology searches were done using makeblastdb (ver. 2.2.28) and HMMER (ver. 3.0) tools. Bioinformatics-based methods allowed us to detect one CRISPR-Cas system in the studied genome of Escherichia coli HS and read the spacer sequences of its CRIPSR array. The protospacer regions complementary to the spacer sequences of the detected CRISPR array are typical for a few types of phages. Based on these findings, one can assess the degree of bacterial resistance to alien genetic elements.

Keywords: bioinformatics, CRISPR-Cas system, Escherichia coli HS, bacteriophage

gg Correspondence should be addressed: Elena Ivanova Timiryazeva 16, Irkutsk, 664003; [email protected]

Received: 15.03.18 Accepted: 24.03.18

DOI: 10.24075/brsmu.2018.019

поиск и анализ crispr-cas системы в штамме ESCHERICHIA COLI hs и детектируемых спейсерами его

crispr-кассеты фаговых рас методами биоинформатики

Е. И. Иванова1 Ю. П. Джиоев2, А. Ю. Борисенко2, Н. П. Перетолчина2, Л. А. Степаненко2, А. И. Парамонов1, Е. В. Григорова1, У. М. Немченко1, Т. В. Туник1, Е. А. Кунгурцева1

1 Научный центр проблем здоровья семьи и репродукции человека, Иркутск

2 Институт биомедицинских технологии, Иркутский государственный медицинский университет, Иркутск

CRISPR-Cas система — это иммунная система прокариот, обеспечивающая защиту от чужеродных репликонов, в первую очередь вирусов и плазмид. Устойчивость к повторным инфекциям приобретается в результате включения в состав CRISPR-кассет коротких последовательностей, или спейсеров, комплементарных участкам соответствующих вирусных или плазмидных геномов. В настоящее время эшерихиозные инфекции остаются серьезной проблемой практической медицины. Вследствие их крайней устойчивости к терапии с использованием антибиотиков необходима разработка новых подходов лечения. Целью исследования был поиск структур CRISPR-Cas систем в геномной последовательности штамма Escherichia coli HS. Использовали методы программного моделирования MacSyFinder (Macromolecular System Finder, ver. 1.0.2.). Поиск точной гомологии последовательностей осуществляли посредством установленных вспомогательных пакетов makeblastdb (ver. 2.2.28), HMMER (ver. 3.0). В результате методами биоинформатики была выявлена одна CRISPR-Cas система и расшифрованы спейсерные последовательности CRISPR-кассеты у штамма Escherichia coli HS. С помощью последовательностей спейсеров CRISPR-кассеты были определены комплементарные им протоспейсерные участки нескольких типов фагов, что позволяет оценить степень их устойчивости к этим чужеродным генетическим элементам.

Ключевые слова: биоинформатика, CRISPR-Cas система, Escherichia coli HS, бактериофаги

[><] Для корреспонденции: Елена Иннокентьевна Иванова

ул. Тимирязева, д. 16, г. Иркутск, 664003; [email protected]

Статья получена: 15.03.18 Статья принята к печати: 24.03.18

DOI: 10.24075/vrgmu.2018.019

The Escherichia coli species comprises multiple biotypes. Some of them are commensal colonizers of the mammalian (including human) gut. Others are pathogenic and cause disease. One of the most significant causative agents of intestinal infections is enterohemorrhagic Escherichia coli O157:H7, whereas an important representative of commensals is E. coli HS. Infection

caused by E. coli O157:H7 can provoke hemolytic uremic syndrome (HUS) characterized by progressive renal failure. E. coli O157:H7 is a serotype capable of producing Shiga toxins [1-3]. No specific treatment has yet proved effective against this syndrome. Only supportive care is recommended during the acute stage of the disease. The use of antibiotics for treating

infections caused by Shiga-toxin-producing E. coli (Stx-E. col!) is very debatable [4, 5]. It has been shown that antibiotic therapy prescribed to patients with acute gastrointestinal infection caused by Stx-E. coli increases the risk of developing HUS 17-fold [6]. Disruption of the bacterial membrane by antibiotics can stimulate progression to the acute stage because the bacteria start to release the toxin in large quantities [7].

Therefore, we need novel alternatives to antibiotics to combat pathogenic bacteria. Phage therapy holds great promise here [8-10]. The evolution of this approach relies on the fundamental knowledge about the genetic basis underlying the interactions between bacteria and bacteriophages. This knowledge, in turn, can be obtained only if bacterial and phage genomes, as well as new analytical methods, are at the researcher's disposal. Currently available bioinformatics software allows the researcher to manipulate huge arrays of genomic data, extracting new information about bacterial genomes [11].

Besides the advances in bioinformatics, another significant event of the past few years is discovery of specific adaptive immunity in prokaryotes. It was long believed that bacteria could not resist phage attacks, but in 1987 a strange region was discovered in the E. coli genome that consisted of multiple repeats [12]. However, it was not until 2005 that it became clear that the sequences alternating with those repeats were often identical to the sequences found in bacterial and plasmid genomes [13, 14]. The discovered structures were termed CRISPR-Cas (Clustered Regularly Interspaced Short Palindromic Repeats — CRISPR-associated proteins). They are a specific adaptive defense of bacteria and archaea against alien genetic material mostly derived from phages and plasmids [15-18]. CRISPR arrays are a unique set of palindromic repeats of 21-47 base pairs separated by unique spacers. Spacers are complementary to the regions in phage or plasmid genomes the bacterium is immune against [13]. In close proximity to a CRISPR locus are cas-genes. Their products ensure proper functioning of a CRISPR locus. According to the current classification, CRISPR-Cas systems are grouped into 3 types based on their mechanism of action and the cas- proteins present in the genome [19].

Bioinformatic methods are employed to detect and identify CRISPR-Cas systems in bacterial genomes [20, 21]. For example, they can help to identify bacteriophage races by bacterial spacer sequences and, therefore, to assess bacterial resistance to certain phages or plasmids [22-24]. This is an important research field, because such screening can provide a solution to the practical challenges faced in the therapy of infections and contribute to the study of evolution across and between bacterial species [17, 22]. For many bacterial species, however, the mechanism of interactions between them and their phages mediated by CRISPR-Cas and anti-CRISPR-Cas systems remains totally understudied. Therefore, it is wise to start with the development of an efficient algorithm for the bioinformatics-based search and analysis of bacterial CRISPR-Cas loci and their structural components and then proceed to the screening of phage races using bacterial CRIPSR arrays. Considering the abovesaid, we aimed to search the genome of Escherichia coli HS for CRIPSR-Cas loci, study the detected components and then identify the corresponding bacteriophage races through screening using bacterial CRISPR arrays and an original bioinformatics-based search algorithm.

METHODS

The object of our study was the strain Escherichia coli HS. GenBank stores two of its genomes: NC_009800.1 sequenced

in 2017 and CP000802 sequenced in 2014. E. coli HS represented in GenBank by the genome NC_009800.1 was cultured using a reference strain from the collection of the Center for Vaccine Development (USA) [25]. For our study we selected the genome CP000802 of a reference strain [26] isolated from the gastrointestinal tract of a healthy human who showed no clinical symptoms of colonization [25].

To detect CRISPR-Cas loci in the bacterial genome, we used MacSyFinder (Macromolecular System Finder, ver. 1.0.2.), a program for bioinformatic modelling [27]. This software requires a protein profile of genomic sequences encoded as hidden Markov models (HMM) available in PFAM, TIGRFAM and PRODOM databases. Sequence homology searches were conducted using makeblastdb (ver. 2.2.28) and HMMER (ver. 3.0); the same software allowed us to obtain structural and functional characteristics of cas-proteins detected in each analyzed genome [28]. Visual representation of the results returned by MacSyFinder was generated in MacSyView. The programming language used was Python (ver. 2.7) [29].

The obtained CRISPR arrays were run against the online database CRISPI: a CRISPR Interactive database (Gen Ouest Bioinformatics Platform, http://genouest.org/) for structural analysis. Bacterial and archaeal genomes were downloaded from the NCBI FTP Server and processed in C and Java (ver. 1.5.0.12.) [30]. The detection algorithm was based on imposing a limitation on the number of closest matches. To avoid detection of unrelated structures, the minimally required percent identity was set to 60%. The web-page was implemented in PHP (ver. 4.3.9) and Java (ver. 1.5.0.12). For phage identification, the obtained spacer sequences were run against the GenBank-Phage database using the search algorithm BLASTn [31]. The following online services were used: CRISPRTarget (http:// bioanalysis.otago.ac.nz/CRISPRTarget/crispr_analysis.html) and Mycobacteriophage Database (http://phagesdb.org/blast/).

RESULTS

The screening of the E. coli HS genome CP000802 revealed a presence of a CRISPR-Cas system at positions 29206522921839, i.e. its length was 1,187 b.p. Structurally, this CRISPR-Cas system belonged to CAS-Type-IE.

Using MacSyFinder, we identified and visualized the following regions of the E. coli HS genome coding for Cas proteins:

- mandatory genes, whose presence in the genome indicates the presence of a CRISPR-Cas system (Fig. 1);

- accessory genes that may be found in more than one system and are hard to identify using only one protein profile; however, they also signal the presence of a CRISPR-Cas system in a bacterial genome.

Using MacSyFinder, we were able to detect cas-genes in the CRISPR-Cas system of the analyzed E. coli HS genome and get a visual representation of the obtained XML in MacSyView. Examples of cas-genes and their location in the genome of the studied strain are shown in Fig. 1.

Using HMMER (ver. 3.0) and makeblastdb (ver. 2.2.28), we obtained structural and functional characteristics of cas-proteins detected in each analyzed genome, namely: gene (the gene corresponding to the profile), system (the system the gene belongs to), hitid (the sequence identifier), hit seq length (length of the sequence), replicon name (the name of the replicon), position hit (the rank of the sequence matched in the input dataset file), i-eval (independent evalue), score (the score of the hit), profile coverage (percentage of the profile that matches the hit sequence), sequence coverage (percentage of the hit

sequence that matches the profile), begin match (the position in the sequence where the profile match begins), and end match (the position in the sequence where the profile match ends) (Fig. 2).

The obtained CRISPR arrays were analyzed in real time in CRISPI: a CRISPR Interactive database, which basically uses homology of repeated regions to return information about

mandatory

the sequence structure. Using this online tool, 11 repeats were identified in the CRISPR array of the studied strain. The consensus view is provided in Fig. 3. After repeats were detected, 10 spacers were identified in the CRISPR array (Table 1). Visual representations of the CRISPR array and cas-genes detected in the studied bacterial genome was implemented in Java (Fig. 4).

w

accessory

iii ii ii ii iihmmilin

Fig. 1. Cas-genes (A) and their location in the genome (B) of E. coli HS (CP000802) detected by MacSyFinder and visualized in MacSyView

Color Sequence Id Position Profile Match Gana Function status System length (") Score ¡••value Profile cove iag e coverage Begin End

lel|NC_0Q2695.1 _prol_NP_311635.1. .3467 3467 cís2_TypelE mandatory CAS-TypelE 97 133.6 5.6*40 1.00 0.89 3 88

Icl| NC_00269 5.1 _pí'ot_NP_311636.1. .3468 3468 cas1_Type1E mandatory CAS-TypelE 307 380.1 1.4e-114 0.99 0.86 8 272

lcl|NC_002695.1 _prot_NP_311637 1 .3469 3469 cas8_TypelE mandatory CAS-TypelE 216 292.6 Te-88 1.00 0.98 1 212

lcl|NC_002695.1_prot_NP_311638.1. .3470 3470 cas5_Type1E mandaiory CAS-TypelE 248 159.4 3e-47 0.99 0.93 .3 233

lcl|NC_002695.1„prot_NP,311635.1. .3471 3471 cas7^Type!E mandatory CA5-TypelE 351 447.9 9.3e-135 1.00 0.92 3 324

leí | N C_002695,1 _proi_WP_311640 1. .3472 3472 cs«2_TypelE mandaiory CAS-TypelE 178 127.7 1.3e-37 1.00 0.89 12 169

■ lcl|NC_002695.1_prot_MP_3l 1641.1. .3473 3473 csel_TypeJE mandatory CAS-TypelE 520 620.3 7.7 e-187 1.00 0.97 5 509

lcl|NC_002695.1_prot_NP_311642.1. .3474 3474 cas3_Type1 accessory CAS 885 216.2 1.9e-64 0.90 0.42 292 662

Fig. 2. Structural and functional characteristics of Сas proteins of E. coli HS (CP000802) obtained in MacSyFinder

Crispr info

Escherichia coli 0157 H7 str. Sakai chromosome Kingdom : Bacteria

RefSeq :NC_002695

Consensus and repeat palindromic structure: 20 units

C GGT TTA.T CCC C GCAGGC GCGGGGAACTC {29 bp )

Begin position 2920652 End position 2921839

Consensus view

Fig. 3. The consensus view of the alternating repeats in the genome of E. coli HS (CP000802) generated in CRISPI: a CRISPR Interactive database. The size of nucleotide letter codes shows a degree of nucleotide variability in the repeat: the taller the letter, the more variable the nucleotide

Fig. 4. Location of cas-genes and the CRISPR array in the genome of E. coll HS (CP000802)

Table 1. The list of nucleotide sequences in the CRIPSR array: spacers separated by repeat units detected In CRISPI: a CRISPR Interactive database in the genome of E. coli HS (CP000802)

Spacers/repeats Begin End Nucleotide sequences Size

unit 1 2920652 2920680 ATGGTTATCCCCGCTGACGCGGGGAACTC 29

spacer 1 2920681 2920712 TCGTCCAGACTGAATACGTTGTCCCAAAATCT 31

unit 2 2920713 2920741 CGGTTTATCCCCGCTGGCGCGGGGAACTC 29

spacer 2 2920742 2920773 CTATTGATGAGGTGCACCATCAGAAGCGAGAT 31

unit 3 2920774 2920802 CGGTTTATCCCCGCTGGCGCGGGGAACTC 29

spacer 3 2920803 2920834 GACGTACAGATTGGCTGCGGCACCTCAAACAC 31

unit 4 2920835 2920863 CGGTTTATCCCCGCAGGCGCGGGGAACTC 29

spacer 4 2920864 2920895 TTAATTCGCGTACCTGCGCATCCATTGCCGCG 31

unit 5 2920896 2920924 CGGTTTATCCCCGCAGGCGCGGGGAACTC 28

spacer 5 2920925 2920956 CGCAATCATGTTTTTCATTGGGTTTACGTCCT 31

unit 6 2920957 2920985 CGGTTTATCCCCGCAGGCGCGGGGAACTC 28

spacer 6 2920986 2921017 TTTTTATGACTGAATCCACTACGCCTTCATAG 31

unit 7 2921018 2921046 CGGTTTATCCCCGCAGGCGCGGGGAACTC 28

spacer 7 2921047 2921078 TTTACGTCGTTGATGACATCGTTCAGGTGTTT 31

unit 8 2921079 2921107 CGGTTTATCCCCGCAGGCGCGGGGAACTC 28

spacer 8 2921108 2921139 GTGATTTTCGTACCCGGCGCGATCGCGATATG 31

unit 9 2921140 2921168 CGGTTTATCCCCGCAGGCGCGGGGAACTC 28

spacer 9 2921169 2921200 GATAACCGCTTCGCGGTCAATATCTGCCGCAC 31

unit 10 2921201 2921229 CGGTTTATCCCCGCAGGCGCGGGGAACTC 28

spacer 10 2921230 2921261 GCCCATCGCCTGCGCCACACTGTTAAAAAGTT 31

unit 11 2921262 2921290 CGGTTTATCCCCGCAGGCGCGGGGAACTC 28

spacer 11 2921291 2921322 TCATTCGCAATCATCCACTGACTCAGGGGCTG 31

Table 2. Spectrum of the phage races revealed by the complementary structures of the spacer sequences of the CRISPR cassette of E. coli HS strain (No. CP000802)

№ Spacer Bacteriophages Number of substitutions

1 spacer 1 (2920681-2920712) Aeromonas phage phiAS4, (HM452125) positions: 100313-100337, Cronobacter phage vB_CsaP_Ss1, (KM058087) positions: 19880-19863 8 10

2 spacer 5 (2920925-2920956) Salmonella phage PVP-SE1, (GU070616) positions: 124932-124959 Salmonella phage SSE-121, (JX181824) positions: 87806-87779 Bacillus phage Bp8p-T, (KJ010548) positions: 144792-144820 Bacillus phage Bp8p-C, (KJ010547) positions: 144790-144818 7 7 8 8

3 spacer 7 (2921047-2921078) Rhizobium phage vB_RleM_P10VF, (KM199770) positions: 93101-93076 Burkholderia phage phiE255, (CP000622) positions: 17180-17211 Burkholderia cenocepacia phage BcepMu, (AY539836) positions: 30887-30918 Gordonia phage GTE5, (JF923796) positions: 49708-49734 Dickeya phage vB_DsoM_LIMEstone1 (HE600015) positions: 52018-52038 Dickeya phage RC-2014, (KJ716335) positions: 27496-27516 Synechococcus phage S-CAM1 (HQ634177) positions: 189041-189018 Cyanophage S-SSM6b (HQ316603) positions: 161353-161374 Cyanophage S-SSM4 (HQ316583) positions: 103276-103255 8 7 7 8 8 8 9 10 10

4 spacer 10 (2921230-2921261) Bacteriophage RTP, (AM156909) positions: 34535-34554 10

DISCUSSION

Last year the Escherichia coli HS genome NC_009800.1 was annotated in the GenBank database. The annotation contained information about three CRISPR-Cas loci in this genome. In the CRISPR-Cas database (http://crispr.i2bc.paris-saclay.fr/ crispr/) these loci are represented by a few variants. Our study demonstrates that, on the whole, the structural units of the CRISPR array detected in the E. coli HS genome (CP000802, sequenced in 2014) coincide with the structural units of the E. coli HS strain (NC_009800_6, sequenced in 2017).

Using the spacer sequences detected in the CRIPSR array of the studied strain, we attempted to identify the phages (Table 2). Of 10 spacer sequences only 4 spacers (1, 5, 7, and 10) were complementary to the protospacers of phage races presented in the table. The identified phage races are typical for

a wide range of bacterial hosts. Perhaps, this is a result of the horizontal transfer of CRIPSR-Cas systems between different types of bacteria throughout a long history of development of their adaptive immunity. Further research will definitely yield new knowledge of the nature of the antagonistic relationship between bacteria and their phages. Based on the detected phage races, one can infer the degree of immune protection and the viability of bacteria throughout their evolution.

CONCLUSIONS

The successful detection of a CRISPR-Cas array in the genome of the E. coli HS strain (CP000802, sequenced in 2014) and its structural analysis render bioinformatics-based methods effective for the search of CRISPR-Cas structures in the sequenced bacterial genomes. Such type of search

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yields valuable information. The presence of "mandatory" Cas proteins suggest high anti-phage activity of the CRISPR-Cas system of the studied strain. The number of detected spacers reflect the duration of the strain's evolution. The comparative analysis of spacers in two CRISPR arrays detected in the CP000802 genome of E. coli HS sequenced in 2014 and in the NC_009800.1 genome of the same strain sequenced in

2017 demonstrates that the number of spacers in the CRIPSR array detected in NC_009800.1 has increased to 19. The number of spacers in CP000802 is only 10. We assume that such increase in the number of spacers was possible due to their accumulation following frequent passaging or because of frequent contamination by phages. In any case, it can be indicative of high CRIPSR-Cas activity in E. coli HS.

References

1. Trachtman H, Austin C, Lewinski M, Stahl R. A. Renal and neurological involvement in typical Shiga toxin-associated HUS. Nature Reviews Nephrology. 2012; (8): 658-69.

2. Croxen MA, Law RJ, Scholz R, Keeney KM, Wlodarska M, Finlay BB. Recent advances in understanding enteric pathogenic Escherichia coli. Clinical Microbiology Reviews. 2013; (26): 82280.

3. Tarr G, Shringi S, Phipps AI, Besser TE, Mayer J, Oltean HN, et. al. Geogenomic Segregation and Temporal Trends of Human Pathogenic Escherichia coli O157:H7, Washington, USA, 20052014. Emerg Infect Dis. 2018; 24 (1): 32-9.

4. Ivanova EI, Rychkova LV, Nemchenko UM, Bukharova EV, Savelkaeva MV, Dzhioeva YuP. The Structure of the Intestinal Microbiota of the Intestine and the Frequency of Detection of Pathogenicity Genes (stx1, stx2, bfp) in Escherichia coli with Normal Enzymatic Activity Isolated from Children during the First Year of Life. Molecular Genetics, Microbiology and Virology. 2017; 32 (1): 42-8.

5. Holmes A, Dallman TJ, Shabaan S, Hanson M, Allison L. Validation of Whole-Genome Sequencing for Identification and Characterization of Shiga Toxin-Producing Escherichia coli To Produce Standardized Data To Enable Data Sharing. J Clin Microbiol. 2018; 56 (3): e01388-17.

6. Wong CS, Mooney JC, Brandt JR, Staples AO, Jelacic S, Boster DR, et al. Risk factors for the hemolytic uremic syndrome in children infected with Escherichia coli O157:H7: a multivariable analysis. Clin Infect Dis. 2012; 55 (1): 33-41.

7. Freedman SB, Xie J, Neufeld MS, Hamilton WL, Hartling L, Tarr PI. Shiga toxin-producing Escherichia coli infection, antibiotics, and risk of developing hemolytic uremic syndrome: A meta-analysis. Clinical Infectious Diseases. 2016; (62): 1251-58.

8. Pirnay JP, Blasdel BG, Bretaudeau L, Buckling A, Chanishvili N, Clark JR, et.al. Quality and safety requirements for sustainable phage therapy products. Pharm Res. 2015; 32 (7): 2173-79.

9. Qadir MI. Review: phage therapy: a modern tool to control bacterial infections. Pak J Pharm Sci. 2015; 28 (1): 265-70.

10. Nazarov PA. Al ternativy antibiotikam: liticheskie fermenty bakteriofagov i fagovaja terapija. Vestnik RGMU. 2018; (1).

11. Rusconi B, Sanjar F, Koenig SS, Mammel MK, Tarr PI, Eppinger M. Whole Genome Sequencing for Genomics-Guided Investigations of Escherichia coli O157:H7 Outbreaks. Front Microbiol. 2016; 7: 985.

12. Nakata A, Amemura M, Makino KJ. Unusual nucleotide arrangement with repeated sequences in the Escherichia coli K-12 chromosome. Bacteriol. 1989; 171 (6): 3553-56.

13. Bolotin A, Quinquis B, Sorokin A, Ehrlich SD. Clustered regularly interspaced short palindrome repeats (CRISPRs) have spacers of extrachromosomal origin. Microbiology 2005; 151: 2551-61.

14. Brouns SJ, Jore MM, Lundgren M, Westra ER, Slijkhuis RJ, Snijders AP, et al. CRISPR RNAs guide antiviral defense in prokaryotes. Science. 2008; (321): 960-4.

15. Dzhagarov DJe. Umnye nozhnicy dlja DNK. Himija i zhizn'. 2014; 7: 6-9.

16. Westra ER, Buckling A, Fineran PC. CRISPR-Cas systems: beyond adaptive immunity. Nat Rev Microbiol. 2014; 12 (5): 31726.

17. Makarova KS, Koonin EV. Annotation and Classification of CRISPR-Cas Systems. Methods Mol Biol. 2015; (1311): 47-75.

18. Modell JW, Jiang W, Marraffini LA. CRISPR-Cas systems exploit viral DNA injection to establish and maintain adaptive immunity. Nature. 2017; 544 (7648): 101-4.

19. Gasiunas G, Sinkunas T, Siksnys V. Molecular mechanisms of CRISPR-mediated microbial immunity. Cellular and Molecular Life Sciences. 2014; 71 (3): 449-65.

20. Peretolchina NP, Dzhioev YP, Boricenko AY, Paramonov AI, Voskresenskaya EA, Stepanenko LA, et al. Bioinformatic search and screening of phages and plasmids via spaser sites of Yersinia pseudotuberculosis YPIII CRISPR/Cas System. Materials 15th Medical Biodefence Conference, 26-29 April 2016; Minich: 4344.

21. Zlobin VI, Dzhioev YuP, Peretolchina NP, Borisenko AY, Stepanenko LA, Yingchen Wang, et.al. Prospects to Enhance Phage Therapy by Looking At CRISP Fingerprints in Bacterial Populations. Current Trends in Biomedical Engineering & Biosciences. 2018; 10 (5): 1-3.

22. Abedon S, Kuhl S, Blasdel B. Phage treatment of human infections. Bacteriophage. 2011; (1): 66-85.

23. Stepanenko LA, Dzhioev JuP, Borisenko AJu, Kolbaseeva OV, Zlobin VI, Malov IV. Poisk fagov i plazmid cherez spejsernye sajty CRISPR/CAS-sistemy Neisseria meningitides fdaargos_214. Zhurnal infektologii. 2018; 10 (S1): 30-31.

24. Borisenko AJu, Dzhioev JuP, Peretolchina NP, Stepanenko LA, Kuz'minova VM, Kolbaseeva OV i dr. Bioinformacionnyj poisk i skrining bakteriofagov cherez spejsery CRISPR/CAS-sistemy shtamma Staphylococcus aureus Mu3. Aktual'nye problemy nauki pribajkal ja. 2017; 2: 45-9.

25. Levine M. M., Rennels M. B. E. coli colonization factor antigen in diarrhoea. Lancet. 1978; 2 (8088): 534.

26. Rasko DA, Rosovitz M J, Myers G SA, Mongodin EF, Fricke WF, Gajer P, et al. The Pangenome Structure of Escherichia coli: Comparative Genomic Analysis of E. coli Commensal and Pathogenic Isolates. J Bacteriol. 2008; 190 (20): 6881-93.

27. Abby SS, Néron B, Ménager H, Touchon M, Rocha Eduardo PC. MacSyFinder: A Program to Mine Genomes for Molecular Systems with an Application to CRISPR-Cas Systems. PLoS One. 2014; 9 (10): e110726.

28. Gaj T, Gersbach CA, Barbas CF. ZFN, TALEN and CRISPR/Cas-based methods for genome engineering. Trends Biotechnol. 2013; 31: 397-405.

29. Qi LS, Larson MH, Gilbert LA, Doudna JA, Weissman JS, Arkin AP, et al. Repurposing CRISPR as an RNA guided platform for sequence-specific control of gene expression. Cell. 2013; 152 (5): 1173-83.

30. Grissa I, Vergnaud G, Pourcel C. CRISPRFinder: a web tool to identify clustered regularly interspaced short palindromic repeats. Nucleic Acids Res. 2007; (35) (Web Server issue): W52-7.

31. Johnson M, Zaretskaya I, Raytselis Y, Merezhuk Y, McGinnis S, Madden TL. NCBI BLAST: a better web interface. NCBI BLAST: a better web Interface. Nucleic Acids Res. 2008; (1); 36 (Web Server issue): W5-9.

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