$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

Utilizing Red Spotted Apollo Butterfly Transcriptome to Identify Antimicrobial Peptide Candidates against Porphyromonas gingivalis 원문보기

Insects, v.12 no.5, 2021년, pp.466 -   

Lee, Kang-Woon (Holoce Ecosystem Conservation Research Institute (HECRI), Hweongsung 25257, Gangwon-do, Korea) ,  Kim, Jae-Goo (holoce@hecri.re.kr) ,  Veerappan, Karpagam (Graduate School of Biotechnology, Kyung Hee University, Yongin-si 17104, Gyeonggi-do, Korea) ,  Chung, Hoyong (zxcv913@naver.com) ,  Natarajan, Sathishkumar (3BIGS Co. Ltd., 156, Gwanggyo-ro, Yeongtong-gu, Suwon-si 16506, Gyeonggi-do, Korea) ,  Kim, Ki-Young (karpagam@3bigs.com (K.V.)) ,  Park, Junhyung (hychung@3bigs.com (H.C.))

Abstract AI-Helper 아이콘AI-Helper

Simple SummaryInfections caused by bacteria, fungi, or viruses possess serious threat to human health and life. This is well realized in the current COVID-19 pandemic scenario. Antimicrobial peptides (AMPs) are a natural line of defense in many organisms, especially insects which survive in extreme ...

주제어

참고문헌 (35)

  1. 1. Huan Y. Kong Q. Mou H. Yi H. Antimicrobial Peptides: Classification, Design, Application and Research Progress in Multiple Fields Front. Microbiol. 2020 11 582779 10.3389/fmicb.2020.582779 33178164 

  2. 2. Christaki E. Marcou M. Tofarides A. Antimicrobial Resistance in Bacteria: Mechanisms, Evolution, and Persistence J. Mol. Evol. 2020 88 26 40 10.1007/s00239-019-09914-3 31659373 

  3. 3. Lopez Romo A. Quiros R. Appropriate use of antibiotics: An unmet need Ther. Adv. Urol. 2019 11 1756287219832174 10.1177/1756287219832174 31105775 

  4. 4. Lee J.H. Chung H. Shin Y.P. Kim M.A. Natarajan S. Veerappan K. Kim S.H. Park J. Hwang J.S. Deciphering Novel Antimicrobial Peptides from the Transcriptome of Papilio xuthus Insects 2020 11 776 10.3390/insects11110776 

  5. 5. Lee J.H. Chung H. Shin Y.P. Kim I.W. Natarajan S. Veerappan K. Seo M. Park J. Hwang J.S. Transcriptome Analysis of Psacothea hilaris: De Novo Assembly and Antimicrobial Peptide Prediction Insects 2020 11 676 10.3390/insects11100676 

  6. 6. Park Y. Kim Y. Park G.-W. Lee J.-O. Lee K.-W. Supercooling capacity along with up-regulation of glycerol content in an overwintering butterfly, Parnassius bremeri J. Asia-Pac. Entomol. 2017 20 949 954 10.1016/j.aspen.2017.06.014 

  7. 7. Brady D. Grapputo A. Romoli O. Sandrelli F. Insect Cecropins, Antimicrobial Peptides with Potential Therapeutic Applications Int. J. Mol. Sci. 2019 20 5862 10.3390/ijms20235862 

  8. 8. Buonocore F. Fausto A.M. Della Pelle G. Roncevic T. Gerdol M. Picchietti S. Attacins: A Promising Class of Insect Antimicrobial Peptides Antibiotics 2021 10 212 10.3390/antibiotics10020212 33672685 

  9. 9. Badapanda C. Chikara S.K. Lepidopteran Antimicrobial Peptides (AMPs): Overview, Regulation, Modes of Action, and Therapeutic Potentials of Insect-Derived AMPs Short Views on Insect Genomics and Proteomics: Insect Proteomics Raman C. Goldsmith M.R. Agunbiade T.A. Springer International Publishing Cham, Switzerland 2016 Volume 2 141 163 10.1007/978-3-319-24244-6_6 

  10. 10. Willis J.R. Gabaldon T. The Human Oral Microbiome in Health and Disease: From Sequences to Ecosystems Microorganisms 2020 8 308 10.3390/microorganisms8020308 

  11. 11. Suwandecha T. Srichana T. Balekar N. Nakpheng T. Pangsomboon K. Novel antimicrobial peptide specifically active against Porphyromonas gingivalis Arch. Microbiol. 2015 197 899 909 10.1007/s00203-015-1126-z 26041027 

  12. 12. Wang H. Ai L. Zhang Y. Cheng J. Yu H. Li C. Zhang D. Pan Y. Lin L. The Effects of Antimicrobial Peptide Nal-P-113 on Inhibiting Periodontal Pathogens and Improving Periodontal Status Biomed Res. Int. 2018 2018 1805793 10.1155/2018/1805793 29736391 

  13. 13. Taniguchi M. Ochiai A. Takahashi K. Nakamichi S.I. Nomoto T. Saitoh E. Kato T. Tanaka T. Antimicrobial activity against Porphyromonas gingivalis and mechanism of action of the cationic octadecapeptide AmyI-1-18 and its amino acid-substituted analogs J. Biosci. Bioeng. 2016 122 652 659 10.1016/j.jbiosc.2016.05.008 27478151 

  14. 14. FASTQC Available online: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (accessed on 16 April 2021) 

  15. 15. Bolger A.M. Lohse M. Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data Bioinformatics 2014 30 2114 2120 10.1093/bioinformatics/btu170 24695404 

  16. 16. Haas B.J. Papanicolaou A. Yassour M. Grabherr M. Blood P.D. Bowden J. Couger M.B. Eccles D. Li B. Lieber M. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis Nat. Protoc. 2013 8 1494 1512 10.1038/nprot.2013.084 23845962 

  17. 17. Li W. Godzik A. Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences Bioinformatics 2006 22 1658 1659 10.1093/bioinformatics/btl158 16731699 

  18. 18. Nishimura O. Hara Y. Kuraku S. gVolante for standardizing completeness assessment of genome and transcriptome assemblies Bioinformatics 2017 33 3635 3637 10.1093/bioinformatics/btx445 29036533 

  19. 19. Madeira F. Park Y.M. Lee J. Buso N. Gur T. Madhusoodanan N. Basutkar P. Tivey A.R.N. Potter S.C. Finn R.D. The EMBL-EBI search and sequence analysis tools APIs in 2019 Nucleic Acids Res. 2019 47 W636 W641 10.1093/nar/gkz268 30976793 

  20. 20. Fernandez-Escamilla A.M. Rousseau F. Schymkowitz J. Serrano L. Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins Nat. Biotechnol. 2004 22 1302 1306 10.1038/nbt1012 15361882 

  21. 21. Conchillo-Sole O. de Groot N.S. Aviles F.X. Vendrell J. Daura X. Ventura S. AGGRESCAN: A server for the prediction and evaluation of "hot spots" of aggregation in polypeptides BMC Bioinform. 2007 8 65 10.1186/1471-2105-8-65 17324296 

  22. 22. Torrent M. Di Tommaso P. Pulido D. Nogues M.V. Notredame C. Boix E. Andreu D. AMPA: An automated web server for prediction of protein antimicrobial regions Bioinformatics 2012 28 130 131 10.1093/bioinformatics/btr604 22053077 

  23. 23. Waghu F.H. Gopi L. Barai R.S. Ramteke P. Nizami B. Idicula-Thomas S. CAMP: Collection of sequences and structures of antimicrobial peptides Nucleic Acids Res. 2014 42 D1154 D1158 10.1093/nar/gkt1157 24265220 

  24. 24. Lee H.T. Lee C.C. Yang J.R. Lai J.Z. Chang K.Y. A large-scale structural classification of antimicrobial peptides Biomed Res. Int. 2015 2015 475062 10.1155/2015/475062 26000295 

  25. 25. Wang G. Li X. Wang Z. APD3: The antimicrobial peptide database as a tool for research and education Nucleic Acids Res. 2016 44 D1087 D1093 10.1093/nar/gkv1278 26602694 

  26. 26. Kim J. Park S. Shin Y.-K. Kang H. Kim K.-Y. In vitro antibacterial activity of macelignan and corosolic acid against the bacterial bee pathogens Paenibacillus larvae and Melissococcus plutonius Acta Vet. Brno 2018 87 277 284 10.2754/avb201887030277 

  27. 27. Park S. Kim J. Shin Y.-K. Kim K.-Y. Antimicrobial activity of 4-hydroxyderricin, sophoraflavanone G, acetylshikonin, and kurarinone against the bee pathogenic bacteria Paenibacillus larvae and Melissococcus plutonius J. Apic. Res. 2020 1 5 

  28. 28. Jenssen H. Hamill P. Hancock R.E. Peptide antimicrobial agents Clin. Microbiol. Rev. 2006 19 491 511 10.1128/CMR.00056-05 16847082 

  29. 29. Mahlapuu M. Hakansson J. Ringstad L. Bjorn C. Antimicrobial Peptides: An Emerging Category of Therapeutic Agents Front. Cell. Infect. Microbiol. 2016 6 194 10.3389/fcimb.2016.00194 28083516 

  30. 30. Pasupuleti M. Schmidtchen A. Malmsten M. Antimicrobial peptides: Key components of the innate immune system Crit. Rev. Biotechnol. 2012 32 143 171 10.3109/07388551.2011.594423 22074402 

  31. 31. Torrent M. Andreu D. Nogues V.M. Boix E. Connecting peptide physicochemical and antimicrobial properties by a rational prediction model PLoS ONE 2011 6 e16968 10.1371/journal.pone.0016968 21347392 

  32. 32. Bostanci N. Belibasakis G.N. Porphyromonas gingivalis: An invasive and evasive opportunistic oral pathogen FEMS Microbiol. Lett. 2012 333 1 9 10.1111/j.1574-6968.2012.02579.x 22530835 

  33. 33. Xu W. Zhou W. Wang H. Liang S. Roles of Porphyromonas gingivalis and its virulence factors in periodontitis Adv. Protein Chem. Struct. Biol. 2020 120 45 84 10.1016/bs.apcsb.2019.12.001 32085888 

  34. 34. Olsen I. Taubman M.A. Singhrao S.K. Porphyromonas gingivalis suppresses adaptive immunity in periodontitis, atherosclerosis, and Alzheimer’s disease J. Oral. Microbiol. 2016 8 33029 10.3402/jom.v8.33029 27882863 

  35. 35. Kanagasingam S. Chukkapalli S.S. Welbury R. Singhrao S.K. Porphyromonas gingivalis is a Strong Risk Factor for Alzheimer’s Disease J. Alzheimers Dis. Rep. 2020 4 501 511 10.3233/ADR-200250 33532698 

관련 콘텐츠

오픈액세스(OA) 유형

GOLD

오픈액세스 학술지에 출판된 논문

이 논문과 함께 이용한 콘텐츠

저작권 관리 안내
섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로