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Wikipedia

Pathogenomics

Pathogenomics is a field which uses high-throughput screening technology and bioinformatics to study encoded microbe resistance, as well as virulence factors (VFs), which enable a microorganism to infect a host and possibly cause disease.[1][2][3][4] This includes studying genomes of pathogens which cannot be cultured outside of a host.[5] In the past, researchers and medical professionals found it difficult to study and understand pathogenic traits of infectious organisms.[6] With newer technology, pathogen genomes can be identified and sequenced in a much shorter time and at a lower cost,[7][8] thus improving the ability to diagnose, treat, and even predict and prevent pathogenic infections and disease.[9] It has also allowed researchers to better understand genome evolution events - gene loss, gain, duplication, rearrangement - and how those events impact pathogen resistance and ability to cause disease.[8] This influx of information has created a need for bioinformatics tools and databases to analyze and make the vast amounts of data accessible to researchers,[10][11] and it has raised ethical questions about the wisdom of reconstructing previously extinct and deadly pathogens in order to better understand virulence.[12]

History edit

During the earlier times when genomics was being studied, scientists found it challenging to sequence genetic information.[13] The field began to explode in 1977 when Fred Sanger, PhD, along with his colleagues, sequenced the DNA-based genome of a bacteriophage, using a method now known as the Sanger Method.[14][15][16] The Sanger Method for sequencing DNA exponentially advanced molecular biology and directly led to the ability to sequence genomes of other organisms, including the complete human genome.[14][15]

The Haemophilus influenza genome was one of the first organism genomes sequenced in 1995 by J. Craig Venter and Hamilton Smith using whole genome shotgun sequencing.[17][15] Since then, newer and more efficient high-throughput sequencing, such as Next Generation Genomic Sequencing (NGS) and Single-Cell Genomic Sequencing, have been developed.[15] While the Sanger method is able to sequence one DNA fragment at a time, NGS technology can sequence thousands of sequences at a time.[18] With the ability to rapidly sequence DNA, new insights developed, such as the discovery that since prokaryotic genomes are more diverse than originally thought, it is necessary to sequence multiple strains in a species rather than only a few.[19] E.coli was an example of why this is important, with genes encoding virulence factors in two strains of the species differing by at least thirty percent.[19] Such knowledge, along with more thorough study of genome gain, loss, and change, is giving researchers valuable insight into how pathogens interact in host environments and how they are able to infect hosts and cause disease.[19][13]

Pathogen Bioinformatics edit

With this high influx of new information, there has arisen a higher demand for bioinformatics so scientists can properly analyze the new data. In response, software and other tools have been developed for this purpose.[10][20] Also, as of 2008, the amount of stored sequences was doubling every 18 months, making urgent the need for better ways to organize data and aid research.[21] In response, many publicly accessible databases and other resources have been created, including the NCBI pathogen detection program, the Pathosystems Resource Integration Centre (PATRIC),[22] Pathogenwatch,[23] the Virulence Factor Database (VFDB) of pathogenic bacteria,[24][3][21] the Victors database of virulence factors in human and animal pathogens.[25] Until 2022, the most sequenced pathogens are Salmonella enterica and E. coli - Shigella. [10] The sequencing technologies, the bioinformatics tools, the databases, statistics related to pathogen genomes and the applications in forensics, epidemiology, clinical practice and food safety have been extensively reviewed.[10]

Microbe analysis edit

Pathogens may be prokaryotic (archaea or bacteria), single-celled eukarya or viruses. Prokaryotic genomes have typically been easier to sequence due to smaller genome size compared to Eukarya. Due to this, there is a bias in reporting pathogenic bacterial behavior. Regardless of this bias in reporting, many of the dynamic genomic events are similar across all the types of pathogen organisms. Genomic evolution occurs via gene gain, gene loss, and genome rearrangement, and these "events" are observed in multiple pathogen genomes, with some bacterial pathogens experiencing all three.[13] Pathogenomics does not focus exclusively on understanding pathogen-host interactions, however. Insight of individual or cooperative pathogen behavior provides knowledge into the development or inheritance of pathogen virulence factors.[13] Through a deeper understanding of the small sub-units that cause infection, it may be possible to develop novel therapeutics that are efficient and cost-effective.[26]

Cause and analysis of genomic diversity edit

Dynamic genomes with high plasticity are necessary to allow pathogens, especially bacteria, to survive in changing environments.[19] With the assistance of high throughput sequencing methods and in silico technologies, it is possible to detect, compare and catalogue many of these dynamic genomic events. Genomic diversity is important when detecting and treating a pathogen since these events can change the function and structure of the pathogen.[27][28] There is a need to analyze more than a single genome sequence of a pathogen species to understand pathogen mechanisms. Comparative genomics is a methodology which allows scientists to compare the genomes of different species and strains.[29] There are several examples of successful comparative genomics studies, among them the analysis of Listeria[30] and Escherichia coli.[31] Some studies have attempted to address the difference between pathogenic and non-pathogenic microbes. This inquiry proves to be difficult, however, since a single bacterial species can have many strains, and the genomic content of each of these strains varies.[31]

Evolutionary dynamics edit

Varying microbe strains and genomic content are caused by different forces, including three specific evolutionary events which have an impact on pathogen resistance and ability to cause disease, a: gene gain, gene loss, and genome rearrangement.[13]

Gene loss and genome decay edit

Gene loss occurs when genes are deleted. The reason why this occurs is still not fully understood,[32] though it most likely involves adaptation to a new environment or ecological niche.[33][34] Some researchers believe gene loss may actually increase fitness and survival among pathogens.[32] In a new environment, some genes may become unnecessary for survival, and so mutations are eventually "allowed" on those genes until they become inactive "pseudogenes."[33] These pseudogenes are observed in organisms such as Shigella flexneri, Salmonella enterica,[35] and Yersinia pestis.[33] Over time, the pseudogenes are deleted, and the organisms become fully dependent on their host as either endosymbionts or obligate intracellular pathogens, as is seen in Buchnera, Myobacterium leprae, and Chlamydia trachomatis.[33] These deleted genes are also called Anti-virulence genes (AVG) since it is thought they may have prevented the organism from becoming pathogenic.[33] In order to be more virulent, infect a host and remain alive, the pathogen had to get rid of those AVGs.[33] The reverse process can happen as well, as was seen during analysis of Listeria strains, which showed that a reduced genome size led to a non-pathogenic Listeria strain from a pathogenic strain.[30] Systems have been developed to detect these pseudogenes/AVGs in a genome sequence.[8]

 
Summary of dynamic genomics events
Gene gain and duplication edit

One of the key forces driving gene gain is thought to be horizontal (lateral) gene transfer (LGT).[36] It is of particular interest in microbial studies because these mobile genetic elements may introduce virulence factors into a new genome.[37] A comparative study conducted by Gill et al. in 2005 postulated that LGT may have been the cause for pathogen variations between Staphylococcus epidermidis and Staphylococcus aureus.[38] There still, however, remains skepticism about the frequency of LGT, its identification, and its impact.[39] New and improved methodologies have been engaged, especially in the study of phylogenetics, to validate the presence and effect of LGT.[40] Gene gain and gene duplication events are balanced by gene loss, such that despite their dynamic nature, the genome of a bacterial species remains approximately the same size.[41]

Genome rearrangement edit

Mobile genetic insertion sequences can play a role in genome rearrangement activities.[42] Pathogens that do not live in an isolated environment have been found to contain a large number of insertion sequence elements and various repetitive segments of DNA.[19] The combination of these two genetic elements is thought help mediate homologous recombination. There are pathogens, such as Burkholderia mallei,[43] and Burkholderia pseudomallei[44] which have been shown to exhibit genome-wide rearrangements due to insertion sequences and repetitive DNA segments.[19] At this time, no studies demonstrate genome-wide rearrangement events directly giving rise to pathogenic behavior in a microbe. This does not mean it is not possible. Genome-wide rearrangements do, however, contribute to the plasticity of bacterial genome, which may prime the conditions for other factors to introduce, or lose, virulence factors.[19]

Single-nucleotide polymorphisms edit

Single Nucleotide Polymorphisms, or SNPs, allow for a wide array of genetic variation among humans as well as pathogens. They allow researchers to estimate a variety of factors: the effects of environmental toxins, how different treatment methods affect the body, and what causes someone's predisposition to illnesses.[45] SNPs play a key role in understanding how and why mutations occur. SNPs also allows for scientists to map genomes and analyze genetic information.[45]

Pan and core genomes edit

 
Pan-genome overview

Pan-genome overview The most recent definition of a bacterial species comes from the pre-genomic era. In 1987, it was proposed that bacterial strains showing >70% DNA·DNA re-association and sharing characteristic phenotypic traits should be considered to be strains of the same species.[46] The diversity within pathogen genomes makes it difficult to identify the total number of genes that are associated within all strains of a pathogen species.[46] It has been thought that the total number of genes associated with a single pathogen species may be unlimited,[46] although some groups are attempting to derive a more empirical value.[47] For this reason, it was necessary to introduce the concept of pan-genomes and core genomes.[48] Pan-genome and core genome literature also tends to have a bias towards reporting on prokaryotic pathogenic organisms. Caution may need to be exercised when extending the definition of a pan-genome or a core-genome to the other pathogenic organisms because there is no formal evidence of the properties of these pan-genomes.[citation needed]

A core genome is the set of genes found across all strains of a pathogen species.[46] A pan-genome is the entire gene pool for that pathogen species, and includes genes that are not shared by all strains.[46] Pan-genomes may be open or closed depending on whether comparative analysis of multiple strains reveals no new genes (closed) or many new genes (open) compared to the core genome for that pathogen species.[13] In the open pan-genome, genes may be further characterized as dispensable or strain specific. Dispensable genes are those found in more than one strain, but not in all strains, of a pathogen species.[48] Strain specific genes are those found only in one strain of a pathogen species.[48] The differences in pan-genomes are reflections of the life style of the organism. For example, Streptococcus agalactiae, which exists in diverse biological niches, has a broader pan-genome when compared with the more environmentally isolated Bacillus anthracis.[19] Comparative genomics approaches are also being used to understand more about the pan-genome.[49] Recent discoveries show that the number of new species continue to grow with an estimated 1031 bacteriophages on the planet with those bacteriophages infecting 1024 others per second, the continuous flow of genetic material being exchanged is difficult to imagine.[46]

Virulence factors edit

Multiple genetic elements of human-affecting pathogens contribute to the transfer of virulence factors: plasmids, pathogenicity island, prophages, bacteriophages, transposons, and integrative and conjugative elements.[13][50] Pathogenicity islands and their detection are the focus of several bioinformatics efforts involved in pathogenomics.[51][52] It is a common belief that "environmental bacterial strains" lack the capacity to harm or do damage to humans. However, recent studies show that bacteria from aquatic environments have acquired pathogenic strains through evolution. This allows for the bacteria to have a wider range in genetic traits and can cause a potential threat to humans from which there is more resistance towards antibiotics.[50]

Microbe-microbe interactions edit

 
Staphylococcus aureus biofilm

Microbe-host interactions tend to overshadow the consideration of microbe-microbe interactions. Microbe-microbe interactions though can lead to chronic states of infirmity that are difficult to understand and treat.[9]

Biofilms edit

Biofilms are an example of microbe-microbe interactions and are thought to be associated with up to 80% of human infections.[53] Recently it has been shown that there are specific genes and cell surface proteins involved in the formation of biofilm.[54] These genes and also surface proteins may be characterized through in silico methods to form an expression profile of biofilm-interacting bacteria.[9] This expression profile may be used in subsequent analysis of other microbes to predict biofilm microbe behaviour, or to understand how to dismantle biofilm formation.[9]

Host microbe analysis edit

Pathogens have the ability to adapt and manipulate host cells, taking full advantage of a host cell's cellular processes and mechanisms.[9]

A microbe may be influenced by hosts to either adapt to its new environment or learn to evade it. An insight into these behaviours will provide beneficial insight for potential therapeutics. The most detailed outline of host-microbe interaction initiatives is outlined by the Pathogenomics European Research Agenda.[9] Its report emphasizes the following features:

 
Summary of host-microbe project goals in the Pathogenomics European Research Agenda[9]
  • Microarray analysis of host and microbe gene expression during infection. This is important for identifying the expression of virulence factors that allow a pathogen to survive a host's defense mechanism.[9] Pathogens tend to undergo an assortment of changed in order to subvert and hosts immune system, in some case favoring a hyper variable genome state.[55] The genomic expression studies will be complemented with protein-protein interaction networks studies.[9]
  • Using RNA interference (RNAi) to identify host cell functions in response to infections. Infection depends on the balance between the characteristics of the host cell and the pathogen cell. In some cases, there can be an overactive host response to infection, such as in meningitis, which can overwhelm the host's body.[9] Using RNA, it will be possible to more clearly identify how a host cell defends itself during times of acute or chronic infection.[56] This has also been applied successfully is Drosophila.[56]
  • Not all microbe interactions in host environment are malicious. Commensal flora, which exists in various environments in animals and humans may actually help combating microbial infections.[9] The human flora, such as the gut for example, is home to a myriad of microbes.[57]

The diverse community within the gut has been heralded to be vital for human health. There are a number of projects under way to better understand the ecosystems of the gut.[58] The sequence of commensal Escherichia coli strain SE11, for example, has already been determined from the faecal matter of a healthy human and promises to be the first of many studies.[59] Through genomic analysis and also subsequent protein analysis, a better understanding of the beneficial properties of commensal flora will be investigated in hopes of understanding how to build a better therapeutic.[60]

Eco-evo perspective edit

The "eco-evo" perspective on pathogen-host interactions emphasizes the influences ecology and the environment on pathogen evolution.[13] The dynamic genomic factors such as gene loss, gene gain and genome rearrangement, are all strongly influenced by changes in the ecological niche where a particular microbial strain resides. Microbes may switch from being pathogenic and non-pathogenic due to changing environments.[30] This was demonstrated during studies of the plague, Yersinia pestis, which apparently evolved from a mild gastrointestinal pathogen to a very highly pathogenic microbe through dynamic genomic events.[61] In order for colonization to occur, there must be changes in biochemical makeup to aid survival in a variety of environments. This is most likely due to a mechanism allowing the cell to sense changes within the environment, thus influencing change in gene expression.[62] Understanding how these strain changes occur from being low or non-pathogenic to being highly pathogenic and vice versa may aid in developing novel therapeutics for microbial infections.[13]

Applications edit

 
Baby receiving immunizations

Human health has greatly improved and the mortality rate has declined substantially since the second world war because of improved hygiene due to changing public health regulations, as well as more readily available vaccines and antibiotics.[63] Pathogenomics will allow scientists to expand what they know about pathogenic and non-pathogenic microbes, thus allowing for new and improved vaccines.[63] Pathogenomics also has wider implication, including preventing bioterrorism.[63]

Reverse vaccinology edit

Reverse vaccinology is relatively new. While research is still being conducted, there have been breakthroughs with pathogens such as Streptococcus and Meningitis.[64] Methods of vaccine production, such as biochemical and serological, are laborious and unreliable. They require the pathogens to be in vitro to be effective.[65] New advances in genomic development help predict nearly all variations of pathogens, thus making advances for vaccines.[65] Protein-based vaccines are being developed to combat resistant pathogens such as Staphylococcus and Chlamydia.[64]

Countering bioterrorism edit

In 2005, the sequence of the 1918 Spanish influenza was completed. Accompanied with phylogenetic analysis, it was possible to supply a detailed account of the virus' evolution and behavior, in particular its adaptation to humans.[66] Following the sequencing of the Spanish influenza, the pathogen was also reconstructed. When inserted into mice, the pathogen proved to be incredibly deadly.[67][12] The 2001 anthrax attacks shed light on the possibility of bioterrorism as being more of a real than imagined threat. Bioterrorism was anticipated in the Iraq war, with soldiers being inoculated for a smallpox attack.[68] Using technologies and insight gained from reconstruction of the Spanish influenza, it may be possible to prevent future deadly planted outbreaks of disease. There is a strong ethical concern however, as to whether the resurrection of old viruses is necessary and whether it does more harm than good.[12][69] The best avenue for countering such threats is coordinating with organizations which provide immunizations. The increased awareness and participation would greatly decrease the effectiveness of a potential epidemic. An addition to this measure would be to monitor natural water reservoirs as a basis to prevent an attack or outbreak. Overall, communication between labs and large organizations, such as Global Outbreak Alert and Response Network (GOARN), can lead to early detection and prevent outbreaks.[63]

See also edit

References edit

  1. ^ Sharma AK, Dhasmana N, Dubey N, Kumar N, Gangwal A, Gupta M, Singh Y (March 2017). "Bacterial Virulence Factors: Secreted for Survival". Indian Journal of Microbiology. 57 (1): 1–10. doi:10.1007/s12088-016-0625-1. PMC 5243249. PMID 28148975.
  2. ^ "How Pathogens Cause Disease | Microbiology". courses.lumenlearning.com. Retrieved 4 November 2019.
  3. ^ a b Yang J, Chen L, Sun L, Yu J, Jin Q (January 2008). "VFDB 2008 release: an enhanced web-based resource for comparative pathogenomics". Nucleic Acids Research. 36 (Database issue): D539-42. doi:10.1093/nar/gkm951. PMC 2238871. PMID 17984080.
  4. ^ Gwinn M, MacCannell D, Armstrong GL (March 2019). "Next-Generation Sequencing of Infectious Pathogens". JAMA. 321 (9): 893–894. doi:10.1001/jama.2018.21669. PMC 6682455. PMID 30763433.
  5. ^ Threats, Institute of Medicine (US) Forum on Microbial (2013). Workshop Overview. National Academies Press (US). Retrieved 8 November 2019.
  6. ^ Ekundayo TC, Okoh AI (2018). "Plesiomonas shigelloides That Were Deemed Inconclusive by Traditional Experimental Approaches". Frontiers in Microbiology. 9: 3077. doi:10.3389/fmicb.2018.03077. PMC 6309461. PMID 30627119.
  7. ^ Threats, Institute of Medicine (US) Forum on Microbial (2013). Workshop Overview. National Academies Press (US). Retrieved 8 November 2019.
  8. ^ a b c Lynch T, Petkau A, Knox N, Graham M, Van Domselaar G (October 2016). "A Primer on Infectious Disease Bacterial Genomics". Clinical Microbiology Reviews. 29 (4): 881–913. doi:10.1128/CMR.00001-16. PMC 5010755. PMID 28590251.
  9. ^ a b c d e f g h i j k Demuth A, Aharonowitz Y, Bachmann TT, Blum-Oehler G, Buchrieser C, Covacci A, et al. (May 2008). "Pathogenomics: an updated European Research Agenda". Infection, Genetics and Evolution. 8 (3): 386–93. doi:10.1016/j.meegid.2008.01.005. hdl:10033/30395. PMID 18321793.
  10. ^ a b c d Amoutzias, Grigorios D.; Nikolaidis, Marios; Hesketh, Andrew (17 May 2022). "The Notable Achievements and the Prospects of Bacterial Pathogen Genomics". Microorganisms. 10 (5): 1040. doi:10.3390/microorganisms10051040. ISSN 2076-2607. PMC 9148168. PMID 35630482.
  11. ^ Vinatzer BA, Heath LS, Almohri HM, Stulberg MJ, Lowe C, Li S (15 May 2019). "Cyberbiosecurity Challenges of Pathogen Genome Databases". Frontiers in Bioengineering and Biotechnology. 7: 106. doi:10.3389/fbioe.2019.00106. PMC 6529814. PMID 31157218.
  12. ^ a b c Kaiser J (October 2005). "Virology. Resurrected influenza virus yields secrets of deadly 1918 pandemic". Science. 310 (5745): 28–9. doi:10.1126/science.310.5745.28. PMID 16210501. S2CID 26252589.
  13. ^ a b c d e f g h i Pallen MJ, Wren BW (October 2007). "Bacterial pathogenomics". Nature. 449 (7164): 835–42. Bibcode:2007Natur.449..835P. doi:10.1038/nature06248. PMID 17943120. S2CID 4313623.
  14. ^ a b Brownlee GG (19 August 2015). "Frederick Sanger CBE CH OM. 13 August 1918 — 19 November 2013". Biographical Memoirs of Fellows of the Royal Society. 61: 437–466. doi:10.1098/rsbm.2015.0013.
  15. ^ a b c d Willey JM (2020). Prescott's microbiology. New York, New York: McGraw-Hill Education. pp. 431–432. ISBN 9781260211887. OCLC 1039422993.
  16. ^ "Timeline: Organisms that have had their genomes sequenced". Your Genome. 19 January 2015. Retrieved 9 November 2019.
  17. ^ Fleischmann RD, Adams MD, White O, Clayton RA, Kirkness EF, Kerlavage AR, et al. (July 1995). "Whole-genome random sequencing and assembly of Haemophilus influenzae Rd". Science. 269 (5223): 496–512. Bibcode:1995Sci...269..496F. doi:10.1126/science.7542800. PMID 7542800.
  18. ^ "Key Differences between next-generation sequencing and Sanger sequencing".
  19. ^ a b c d e f g h Fraser-Liggett CM (December 2005). "Insights on biology and evolution from microbial genome sequencing". Genome Research. 15 (12): 1603–10. doi:10.1101/gr.3724205. PMID 16339357.
  20. ^ Oakeson KF, Wagner JM, Mendenhall M, Rohrwasser A, Atkinson-Dunn R (September 2017). "Bioinformatic Analyses of Whole-Genome Sequence Data in a Public Health Laboratory". Emerging Infectious Diseases. 23 (9): 1441–1445. doi:10.3201/eid2309.170416. PMC 5572866. PMID 28820135.
  21. ^ a b Lathe W, Williams J, Mangan M, Karolchik D (2008). "Genomic data resources: challenges and promises". Nature Education. p. 2.
  22. ^ Davis, James J.; Wattam, Alice R.; Aziz, Ramy K.; Brettin, Thomas; Butler, Ralph; Butler, Rory M.; Chlenski, Philippe; Conrad, Neal; Dickerman, Allan; Dietrich, Emily M.; Gabbard, Joseph L. (8 January 2020). "The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities". Nucleic Acids Research. 48 (D1): D606–D612. doi:10.1093/nar/gkz943. ISSN 1362-4962. PMC 7145515. PMID 31667520.
  23. ^ Argimón, Silvia; Yeats, Corin A.; Goater, Richard J.; Abudahab, Khalil; Taylor, Benjamin; Underwood, Anthony; Sánchez-Busó, Leonor; Wong, Vanessa K.; Dyson, Zoe A.; Nair, Satheesh; Park, Se Eun (17 May 2021). "A global resource for genomic predictions of antimicrobial resistance and surveillance of Salmonella Typhi at pathogenwatch". Nature Communications. 12 (1): 2879. doi:10.1038/s41467-021-23091-2. ISSN 2041-1723. PMC 8128892. PMID 34001879.
  24. ^ "VFDB: Virulence Factors of Bacterial Pathogens". www.mgc.ac.cn. Retrieved 8 November 2019.
  25. ^ Sayers, Samantha; Li, Li; Ong, Edison; Deng, Shunzhou; Fu, Guanghua; Lin, Yu; Yang, Brian; Zhang, Shelley; Fa, Zhenzong; Zhao, Bin; Xiang, Zuoshuang (8 January 2019). "Victors: a web-based knowledge base of virulence factors in human and animal pathogens". Nucleic Acids Research. 47 (D1): D693–D700. doi:10.1093/nar/gky999. ISSN 1362-4962. PMC 6324020. PMID 30365026.
  26. ^ Rappuoli R (March 2001). "Reverse vaccinology, a genome-based approach to vaccine development". Vaccine. 19 (17–19): 2688–91. doi:10.1016/S0264-410X(00)00554-5. PMID 11257410.
  27. ^ "Gene flow | genetics". Encyclopedia Britannica. Retrieved 4 November 2019.
  28. ^ Griffiths AJ, Miller JH, Suzuki DT, Lewontin RC, Gelbart WM (2000). "Sources of variation". An Introduction to Genetic Analysis (7th ed.). W.H. Freeman. ISBN 978-0-7167-3771-1.
  29. ^ "Comparative Genomics Fact Sheet". Genome.gov. Retrieved 13 November 2019.
  30. ^ a b c Hain T, Chatterjee SS, Ghai R, Kuenne CT, Billion A, Steinweg C, et al. (November 2007). "Pathogenomics of Listeria spp". International Journal of Medical Microbiology. 297 (7–8): 541–57. doi:10.1016/j.ijmm.2007.03.016. PMID 17482873.
  31. ^ a b Perna NT, Plunkett G, Burland V, Mau B, Glasner JD, Rose DJ, et al. (January 2001). "Genome sequence of enterohaemorrhagic Escherichia coli O157:H7". Nature. 409 (6819): 529–33. Bibcode:2001Natur.409..529P. doi:10.1038/35054089. PMID 11206551.
  32. ^ a b Koskiniemi S, Sun S, Berg OG, Andersson DI (June 2012). "Selection-driven gene loss in bacteria". PLOS Genetics. 8 (6): e1002787. doi:10.1371/journal.pgen.1002787. PMC 3386194. PMID 22761588.
  33. ^ a b c d e f Bliven KA, Maurelli AT (December 2012). "Antivirulence genes: insights into pathogen evolution through gene loss". Infection and Immunity. 80 (12): 4061–70. doi:10.1128/iai.00740-12. PMC 3497401. PMID 23045475.
  34. ^ Ward PN, Holden MT, Leigh JA, Lennard N, Bignell A, Barron A, et al. (January 2009). "Evidence for niche adaptation in the genome of the bovine pathogen Streptococcus uberis". BMC Genomics. 10: 54. doi:10.1186/1471-2164-10-54. PMC 2657157. PMID 19175920.
  35. ^ Parkhill J, Dougan G, James KD, Thomson NR, Pickard D, Wain J, et al. (October 2001). "Complete genome sequence of a multiple drug resistant Salmonella enterica serovar Typhi CT18". Nature. 413 (6858): 848–52. Bibcode:2001Natur.413..848P. doi:10.1038/35101607. PMID 11677608.
  36. ^ Boucher Y, Douady CJ, Papke RT, Walsh DA, Boudreau ME, Nesbø CL, et al. (2003). "Lateral gene transfer and the origins of prokaryotic groups". Annual Review of Genetics. 37: 283–328. doi:10.1146/annurev.genet.37.050503.084247. PMID 14616063.
  37. ^ Lima WC, Paquola AC, Varani AM, Van Sluys MA, Menck CF (April 2008). "Laterally transferred genomic islands in Xanthomonadales related to pathogenicity and primary metabolism". FEMS Microbiology Letters. 281 (1): 87–97. doi:10.1111/j.1574-6968.2008.01083.x. PMID 18318843.
  38. ^ Gill SR, Fouts DE, Archer GL, Mongodin EF, Deboy RT, Ravel J, et al. (April 2005). "Insights on evolution of virulence and resistance from the complete genome analysis of an early methicillin-resistant Staphylococcus aureus strain and a biofilm-producing methicillin-resistant Staphylococcus epidermidis strain". Journal of Bacteriology. 187 (7): 2426–38. doi:10.1128/JB.187.7.2426-2438.2005. PMC 1065214. PMID 15774886.
  39. ^ Bapteste E, Boucher Y (May 2008). "Lateral gene transfer challenges principles of microbial systematics". Trends in Microbiology. 16 (5): 200–7. doi:10.1016/j.tim.2008.02.005. PMID 18420414.
  40. ^ Huang J, Gogarten JP (July 2006). "Ancient horizontal gene transfer can benefit phylogenetic reconstruction". Trends in Genetics. 22 (7): 361–6. doi:10.1016/j.tig.2006.05.004. PMID 16730850.
  41. ^ Mira A, Ochman H, Moran NA (October 2001). "Deletional bias and the evolution of bacterial genomes". Trends in Genetics. 17 (10): 589–96. doi:10.1016/S0168-9525(01)02447-7. PMID 11585665.
  42. ^ Parkhill J, Wren BW, Thomson NR, Titball RW, Holden MT, Prentice MB, et al. (October 2001). "Genome sequence of Yersinia pestis, the causative agent of plague". Nature. 413 (6855): 523–7. Bibcode:2001Natur.413..523P. doi:10.1038/35097083. PMID 11586360.
  43. ^ Nierman WC, DeShazer D, Kim HS, Tettelin H, Nelson KE, Feldblyum T, et al. (September 2004). "Structural flexibility in the Burkholderia mallei genome". Proceedings of the National Academy of Sciences of the United States of America. 101 (39): 14246–51. Bibcode:2004PNAS..10114246N. doi:10.1073/pnas.0403306101. PMC 521142. PMID 15377793.
  44. ^ Holden MT, Titball RW, Peacock SJ, Cerdeño-Tárraga AM, Atkins T, Crossman LC, et al. (September 2004). "Genomic plasticity of the causative agent of melioidosis, Burkholderia pseudomallei". Proceedings of the National Academy of Sciences of the United States of America. 101 (39): 14240–5. doi:10.1073/pnas.0403302101. PMC 521101. PMID 15377794.
  45. ^ a b "What are single nucleotide polymorphisms (SNPs)?". Genetics Home Reference. Retrieved 8 November 2019.
  46. ^ a b c d e f Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL, et al. (September 2005). "Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial "pan-genome"". Proceedings of the National Academy of Sciences of the United States of America. 102 (39): 13950–5. Bibcode:2005PNAS..10213950T. doi:10.1073/pnas.0506758102. PMC 1216834. PMID 16172379.
  47. ^ Lapierre P, Gogarten JP (March 2009). "Estimating the size of the bacterial pan-genome". Trends in Genetics. 25 (3): 107–10. doi:10.1016/j.tig.2008.12.004. PMID 19168257.
  48. ^ a b c Medini D, Donati C, Tettelin H, Masignani V, Rappuoli R (December 2005). "The microbial pan-genome". Current Opinion in Genetics & Development. 15 (6): 589–94. doi:10.1016/j.gde.2005.09.006. PMID 16185861.
  49. ^ Tettelin H, Riley D, Cattuto C, Medini D (October 2008). "Comparative genomics: the bacterial pan-genome". Current Opinion in Microbiology. 11 (5): 472–7. doi:10.1016/j.mib.2008.09.006. PMID 19086349.
  50. ^ a b Gennari M, Ghidini V, Caburlotto G, Lleo MM (December 2012). "Virulence genes and pathogenicity islands in environmental Vibrio strains nonpathogenic to humans". FEMS Microbiology Ecology. 82 (3): 563–73. doi:10.1111/j.1574-6941.2012.01427.x. PMID 22676367.
  51. ^ Langille MG, Brinkman FS (March 2009). "IslandViewer: an integrated interface for computational identification and visualization of genomic islands". Bioinformatics. 25 (5): 664–5. doi:10.1093/bioinformatics/btp030. PMC 2647836. PMID 19151094.
  52. ^ Guy L (October 2006). "Identification and characterization of pathogenicity and other genomic islands using base composition analyses". Future Microbiology. 1 (3): 309–16. doi:10.2217/17460913.1.3.309. PMID 17661643.
  53. ^ "Research on microbial biofilms (PA-03-047)". NIH, National Heart, Lung, and Blood Institute. 20 December 2002.
  54. ^ Valle J, Vergara-Irigaray M, Merino N, Penadés JR, Lasa I (April 2007). "sigmaB regulates IS256-mediated Staphylococcus aureus biofilm phenotypic variation". Journal of Bacteriology. 189 (7): 2886–96. doi:10.1128/JB.01767-06. PMC 1855799. PMID 17277051.
  55. ^ Hogardt M, Hoboth C, Schmoldt S, Henke C, Bader L, Heesemann J (January 2007). "Stage-specific adaptation of hypermutable Pseudomonas aeruginosa isolates during chronic pulmonary infection in patients with cystic fibrosis". The Journal of Infectious Diseases. 195 (1): 70–80. doi:10.1086/509821. PMID 17152010.
  56. ^ a b Cheng LW, Viala JP, Stuurman N, Wiedemann U, Vale RD, Portnoy DA (September 2005). "Use of RNA interference in Drosophila S2 cells to identify host pathways controlling compartmentalization of an intracellular pathogen". Proceedings of the National Academy of Sciences of the United States of America. 102 (38): 13646–51. Bibcode:2005PNAS..10213646C. doi:10.1073/pnas.0506461102. PMC 1224656. PMID 16157870.
  57. ^ Hattori M, Taylor TD (February 2009). "The human intestinal microbiome: a new frontier of human biology". DNA Research. 16 (1): 1–12. doi:10.1093/dnares/dsn033. PMC 2646358. PMID 19147530.
  58. ^ Hooper LV, Gordon JI (May 2001). "Commensal host-bacterial relationships in the gut". Science. 292 (5519): 1115–8. Bibcode:2001Sci...292.1115H. doi:10.1126/science.1058709. PMID 11352068. S2CID 44645045.
  59. ^ Oshima K, Toh H, Ogura Y, Sasamoto H, Morita H, Park SH, et al. (December 2008). "Complete genome sequence and comparative analysis of the wild-type commensal Escherichia coli strain SE11 isolated from a healthy adult". DNA Research. 15 (6): 375–86. doi:10.1093/dnares/dsn026. PMC 2608844. PMID 18931093.
  60. ^ Zoetendal EG, Rajilic-Stojanovic M, de Vos WM (November 2008). "High-throughput diversity and functionality analysis of the gastrointestinal tract microbiota". Gut. 57 (11): 1605–15. doi:10.1136/gut.2007.133603. PMID 18941009. S2CID 34347318.
  61. ^ Achtman M, Morelli G, Zhu P, Wirth T, Diehl I, Kusecek B, et al. (December 2004). "Microevolution and history of the plague bacillus, Yersinia pestis". Proceedings of the National Academy of Sciences of the United States of America. 101 (51): 17837–42. Bibcode:2004PNAS..10117837A. doi:10.1073/pnas.0408026101. PMC 535704. PMID 15598742.
  62. ^ Oyston PC, Dorrell N, Williams K, Li SR, Green M, Titball RW, Wren BW (June 2000). "The response regulator PhoP is important for survival under conditions of macrophage-induced stress and virulence in Yersinia pestis". Infection and Immunity. 68 (6): 3419–25. doi:10.1128/IAI.68.6.3419-3425.2000. PMC 97616. PMID 10816493.
  63. ^ a b c d Pompe S, Simon J, Wiedemann PM, Tannert C (July 2005). "Future trends and challenges in pathogenomics. A Foresight study". EMBO Reports. 6 (7): 600–5. doi:10.1038/sj.embor.7400472. PMC 1369123. PMID 15995675.
  64. ^ a b Sette A, Rappuoli R (October 2010). "Reverse vaccinology: developing vaccines in the era of genomics". Immunity. 33 (4): 530–41. doi:10.1016/j.immuni.2010.09.017. PMC 3320742. PMID 21029963.
  65. ^ a b Rappuoli R (October 2000). "Reverse vaccinology". Current Opinion in Microbiology. 3 (5): 445–50. doi:10.1016/S1369-5274(00)00119-3. PMID 11050440.
  66. ^ Taubenberger JK, Reid AH, Lourens RM, Wang R, Jin G, Fanning TG (October 2005). "Characterization of the 1918 influenza virus polymerase genes". Nature. 437 (7060): 889–93. Bibcode:2005Natur.437..889T. doi:10.1038/nature04230. PMID 16208372. S2CID 4405787.
  67. ^ Tumpey TM, Basler CF, Aguilar PV, Zeng H, Solórzano A, Swayne DE, et al. (October 2005). "Characterization of the reconstructed 1918 Spanish influenza pandemic virus". Science. 310 (5745): 77–80. Bibcode:2005Sci...310...77T. CiteSeerX 10.1.1.418.9059. doi:10.1126/science.1119392. PMID 16210530. S2CID 14773861.
  68. ^ "Terrorism Project)". Center for Defense Information. 20 December 2002.
  69. ^ van Aken J (January 2007). "Ethics of reconstructing Spanish flu: is it wise to resurrect a deadly virus?". Heredity. 98 (1): 1–2. doi:10.1038/sj.hdy.6800911. PMID 17035950. S2CID 32686445.

pathogenomics, field, which, uses, high, throughput, screening, technology, bioinformatics, study, encoded, microbe, resistance, well, virulence, factors, which, enable, microorganism, infect, host, possibly, cause, disease, this, includes, studying, genomes, . Pathogenomics is a field which uses high throughput screening technology and bioinformatics to study encoded microbe resistance as well as virulence factors VFs which enable a microorganism to infect a host and possibly cause disease 1 2 3 4 This includes studying genomes of pathogens which cannot be cultured outside of a host 5 In the past researchers and medical professionals found it difficult to study and understand pathogenic traits of infectious organisms 6 With newer technology pathogen genomes can be identified and sequenced in a much shorter time and at a lower cost 7 8 thus improving the ability to diagnose treat and even predict and prevent pathogenic infections and disease 9 It has also allowed researchers to better understand genome evolution events gene loss gain duplication rearrangement and how those events impact pathogen resistance and ability to cause disease 8 This influx of information has created a need for bioinformatics tools and databases to analyze and make the vast amounts of data accessible to researchers 10 11 and it has raised ethical questions about the wisdom of reconstructing previously extinct and deadly pathogens in order to better understand virulence 12 Contents 1 History 2 Pathogen Bioinformatics 3 Microbe analysis 3 1 Cause and analysis of genomic diversity 3 1 1 Evolutionary dynamics 3 1 1 1 Gene loss and genome decay 3 1 1 2 Gene gain and duplication 3 1 1 3 Genome rearrangement 3 1 1 4 Single nucleotide polymorphisms 3 1 2 Pan and core genomes 3 1 3 Virulence factors 3 2 Microbe microbe interactions 3 2 1 Biofilms 4 Host microbe analysis 4 1 Eco evo perspective 5 Applications 5 1 Reverse vaccinology 5 2 Countering bioterrorism 6 See also 7 ReferencesHistory editDuring the earlier times when genomics was being studied scientists found it challenging to sequence genetic information 13 The field began to explode in 1977 when Fred Sanger PhD along with his colleagues sequenced the DNA based genome of a bacteriophage using a method now known as the Sanger Method 14 15 16 The Sanger Method for sequencing DNA exponentially advanced molecular biology and directly led to the ability to sequence genomes of other organisms including the complete human genome 14 15 The Haemophilus influenza genome was one of the first organism genomes sequenced in 1995 by J Craig Venter and Hamilton Smith using whole genome shotgun sequencing 17 15 Since then newer and more efficient high throughput sequencing such as Next Generation Genomic Sequencing NGS and Single Cell Genomic Sequencing have been developed 15 While the Sanger method is able to sequence one DNA fragment at a time NGS technology can sequence thousands of sequences at a time 18 With the ability to rapidly sequence DNA new insights developed such as the discovery that since prokaryotic genomes are more diverse than originally thought it is necessary to sequence multiple strains in a species rather than only a few 19 E coli was an example of why this is important with genes encoding virulence factors in two strains of the species differing by at least thirty percent 19 Such knowledge along with more thorough study of genome gain loss and change is giving researchers valuable insight into how pathogens interact in host environments and how they are able to infect hosts and cause disease 19 13 Pathogen Bioinformatics editWith this high influx of new information there has arisen a higher demand for bioinformatics so scientists can properly analyze the new data In response software and other tools have been developed for this purpose 10 20 Also as of 2008 the amount of stored sequences was doubling every 18 months making urgent the need for better ways to organize data and aid research 21 In response many publicly accessible databases and other resources have been created including the NCBI pathogen detection program the Pathosystems Resource Integration Centre PATRIC 22 Pathogenwatch 23 the Virulence Factor Database VFDB of pathogenic bacteria 24 3 21 the Victors database of virulence factors in human and animal pathogens 25 Until 2022 the most sequenced pathogens are Salmonella enterica and E coli Shigella 10 The sequencing technologies the bioinformatics tools the databases statistics related to pathogen genomes and the applications in forensics epidemiology clinical practice and food safety have been extensively reviewed 10 Microbe analysis editPathogens may be prokaryotic archaea or bacteria single celled eukarya or viruses Prokaryotic genomes have typically been easier to sequence due to smaller genome size compared to Eukarya Due to this there is a bias in reporting pathogenic bacterial behavior Regardless of this bias in reporting many of the dynamic genomic events are similar across all the types of pathogen organisms Genomic evolution occurs via gene gain gene loss and genome rearrangement and these events are observed in multiple pathogen genomes with some bacterial pathogens experiencing all three 13 Pathogenomics does not focus exclusively on understanding pathogen host interactions however Insight of individual or cooperative pathogen behavior provides knowledge into the development or inheritance of pathogen virulence factors 13 Through a deeper understanding of the small sub units that cause infection it may be possible to develop novel therapeutics that are efficient and cost effective 26 Cause and analysis of genomic diversity edit Dynamic genomes with high plasticity are necessary to allow pathogens especially bacteria to survive in changing environments 19 With the assistance of high throughput sequencing methods and in silico technologies it is possible to detect compare and catalogue many of these dynamic genomic events Genomic diversity is important when detecting and treating a pathogen since these events can change the function and structure of the pathogen 27 28 There is a need to analyze more than a single genome sequence of a pathogen species to understand pathogen mechanisms Comparative genomics is a methodology which allows scientists to compare the genomes of different species and strains 29 There are several examples of successful comparative genomics studies among them the analysis of Listeria 30 and Escherichia coli 31 Some studies have attempted to address the difference between pathogenic and non pathogenic microbes This inquiry proves to be difficult however since a single bacterial species can have many strains and the genomic content of each of these strains varies 31 Evolutionary dynamics edit Varying microbe strains and genomic content are caused by different forces including three specific evolutionary events which have an impact on pathogen resistance and ability to cause disease a gene gain gene loss and genome rearrangement 13 Gene loss and genome decay edit Gene loss occurs when genes are deleted The reason why this occurs is still not fully understood 32 though it most likely involves adaptation to a new environment or ecological niche 33 34 Some researchers believe gene loss may actually increase fitness and survival among pathogens 32 In a new environment some genes may become unnecessary for survival and so mutations are eventually allowed on those genes until they become inactive pseudogenes 33 These pseudogenes are observed in organisms such as Shigella flexneri Salmonella enterica 35 and Yersinia pestis 33 Over time the pseudogenes are deleted and the organisms become fully dependent on their host as either endosymbionts or obligate intracellular pathogens as is seen in Buchnera Myobacterium leprae and Chlamydia trachomatis 33 These deleted genes are also called Anti virulence genes AVG since it is thought they may have prevented the organism from becoming pathogenic 33 In order to be more virulent infect a host and remain alive the pathogen had to get rid of those AVGs 33 The reverse process can happen as well as was seen during analysis of Listeria strains which showed that a reduced genome size led to a non pathogenic Listeria strain from a pathogenic strain 30 Systems have been developed to detect these pseudogenes AVGs in a genome sequence 8 nbsp Summary of dynamic genomics events Gene gain and duplication edit One of the key forces driving gene gain is thought to be horizontal lateral gene transfer LGT 36 It is of particular interest in microbial studies because these mobile genetic elements may introduce virulence factors into a new genome 37 A comparative study conducted by Gill et al in 2005 postulated that LGT may have been the cause for pathogen variations between Staphylococcus epidermidis and Staphylococcus aureus 38 There still however remains skepticism about the frequency of LGT its identification and its impact 39 New and improved methodologies have been engaged especially in the study of phylogenetics to validate the presence and effect of LGT 40 Gene gain and gene duplication events are balanced by gene loss such that despite their dynamic nature the genome of a bacterial species remains approximately the same size 41 Genome rearrangement edit Mobile genetic insertion sequences can play a role in genome rearrangement activities 42 Pathogens that do not live in an isolated environment have been found to contain a large number of insertion sequence elements and various repetitive segments of DNA 19 The combination of these two genetic elements is thought help mediate homologous recombination There are pathogens such as Burkholderia mallei 43 and Burkholderia pseudomallei 44 which have been shown to exhibit genome wide rearrangements due to insertion sequences and repetitive DNA segments 19 At this time no studies demonstrate genome wide rearrangement events directly giving rise to pathogenic behavior in a microbe This does not mean it is not possible Genome wide rearrangements do however contribute to the plasticity of bacterial genome which may prime the conditions for other factors to introduce or lose virulence factors 19 Single nucleotide polymorphisms edit Single Nucleotide Polymorphisms or SNPs allow for a wide array of genetic variation among humans as well as pathogens They allow researchers to estimate a variety of factors the effects of environmental toxins how different treatment methods affect the body and what causes someone s predisposition to illnesses 45 SNPs play a key role in understanding how and why mutations occur SNPs also allows for scientists to map genomes and analyze genetic information 45 Pan and core genomes edit nbsp Pan genome overview Pan genome overview The most recent definition of a bacterial species comes from the pre genomic era In 1987 it was proposed that bacterial strains showing gt 70 DNA DNA re association and sharing characteristic phenotypic traits should be considered to be strains of the same species 46 The diversity within pathogen genomes makes it difficult to identify the total number of genes that are associated within all strains of a pathogen species 46 It has been thought that the total number of genes associated with a single pathogen species may be unlimited 46 although some groups are attempting to derive a more empirical value 47 For this reason it was necessary to introduce the concept of pan genomes and core genomes 48 Pan genome and core genome literature also tends to have a bias towards reporting on prokaryotic pathogenic organisms Caution may need to be exercised when extending the definition of a pan genome or a core genome to the other pathogenic organisms because there is no formal evidence of the properties of these pan genomes citation needed A core genome is the set of genes found across all strains of a pathogen species 46 A pan genome is the entire gene pool for that pathogen species and includes genes that are not shared by all strains 46 Pan genomes may be open or closed depending on whether comparative analysis of multiple strains reveals no new genes closed or many new genes open compared to the core genome for that pathogen species 13 In the open pan genome genes may be further characterized as dispensable or strain specific Dispensable genes are those found in more than one strain but not in all strains of a pathogen species 48 Strain specific genes are those found only in one strain of a pathogen species 48 The differences in pan genomes are reflections of the life style of the organism For example Streptococcus agalactiae which exists in diverse biological niches has a broader pan genome when compared with the more environmentally isolated Bacillus anthracis 19 Comparative genomics approaches are also being used to understand more about the pan genome 49 Recent discoveries show that the number of new species continue to grow with an estimated 1031 bacteriophages on the planet with those bacteriophages infecting 1024 others per second the continuous flow of genetic material being exchanged is difficult to imagine 46 Virulence factors edit Multiple genetic elements of human affecting pathogens contribute to the transfer of virulence factors plasmids pathogenicity island prophages bacteriophages transposons and integrative and conjugative elements 13 50 Pathogenicity islands and their detection are the focus of several bioinformatics efforts involved in pathogenomics 51 52 It is a common belief that environmental bacterial strains lack the capacity to harm or do damage to humans However recent studies show that bacteria from aquatic environments have acquired pathogenic strains through evolution This allows for the bacteria to have a wider range in genetic traits and can cause a potential threat to humans from which there is more resistance towards antibiotics 50 Microbe microbe interactions edit nbsp Staphylococcus aureus biofilm Microbe host interactions tend to overshadow the consideration of microbe microbe interactions Microbe microbe interactions though can lead to chronic states of infirmity that are difficult to understand and treat 9 Biofilms edit Biofilms are an example of microbe microbe interactions and are thought to be associated with up to 80 of human infections 53 Recently it has been shown that there are specific genes and cell surface proteins involved in the formation of biofilm 54 These genes and also surface proteins may be characterized through in silico methods to form an expression profile of biofilm interacting bacteria 9 This expression profile may be used in subsequent analysis of other microbes to predict biofilm microbe behaviour or to understand how to dismantle biofilm formation 9 Host microbe analysis editPathogens have the ability to adapt and manipulate host cells taking full advantage of a host cell s cellular processes and mechanisms 9 A microbe may be influenced by hosts to either adapt to its new environment or learn to evade it An insight into these behaviours will provide beneficial insight for potential therapeutics The most detailed outline of host microbe interaction initiatives is outlined by the Pathogenomics European Research Agenda 9 Its report emphasizes the following features nbsp Summary of host microbe project goals in the Pathogenomics European Research Agenda 9 Microarray analysis of host and microbe gene expression during infection This is important for identifying the expression of virulence factors that allow a pathogen to survive a host s defense mechanism 9 Pathogens tend to undergo an assortment of changed in order to subvert and hosts immune system in some case favoring a hyper variable genome state 55 The genomic expression studies will be complemented with protein protein interaction networks studies 9 Using RNA interference RNAi to identify host cell functions in response to infections Infection depends on the balance between the characteristics of the host cell and the pathogen cell In some cases there can be an overactive host response to infection such as in meningitis which can overwhelm the host s body 9 Using RNA it will be possible to more clearly identify how a host cell defends itself during times of acute or chronic infection 56 This has also been applied successfully is Drosophila 56 Not all microbe interactions in host environment are malicious Commensal flora which exists in various environments in animals and humans may actually help combating microbial infections 9 The human flora such as the gut for example is home to a myriad of microbes 57 The diverse community within the gut has been heralded to be vital for human health There are a number of projects under way to better understand the ecosystems of the gut 58 The sequence of commensal Escherichia coli strain SE11 for example has already been determined from the faecal matter of a healthy human and promises to be the first of many studies 59 Through genomic analysis and also subsequent protein analysis a better understanding of the beneficial properties of commensal flora will be investigated in hopes of understanding how to build a better therapeutic 60 Eco evo perspective edit The eco evo perspective on pathogen host interactions emphasizes the influences ecology and the environment on pathogen evolution 13 The dynamic genomic factors such as gene loss gene gain and genome rearrangement are all strongly influenced by changes in the ecological niche where a particular microbial strain resides Microbes may switch from being pathogenic and non pathogenic due to changing environments 30 This was demonstrated during studies of the plague Yersinia pestis which apparently evolved from a mild gastrointestinal pathogen to a very highly pathogenic microbe through dynamic genomic events 61 In order for colonization to occur there must be changes in biochemical makeup to aid survival in a variety of environments This is most likely due to a mechanism allowing the cell to sense changes within the environment thus influencing change in gene expression 62 Understanding how these strain changes occur from being low or non pathogenic to being highly pathogenic and vice versa may aid in developing novel therapeutics for microbial infections 13 Applications edit nbsp Baby receiving immunizations Human health has greatly improved and the mortality rate has declined substantially since the second world war because of improved hygiene due to changing public health regulations as well as more readily available vaccines and antibiotics 63 Pathogenomics will allow scientists to expand what they know about pathogenic and non pathogenic microbes thus allowing for new and improved vaccines 63 Pathogenomics also has wider implication including preventing bioterrorism 63 Reverse vaccinology edit Reverse vaccinology is relatively new While research is still being conducted there have been breakthroughs with pathogens such as Streptococcus and Meningitis 64 Methods of vaccine production such as biochemical and serological are laborious and unreliable They require the pathogens to be in vitro to be effective 65 New advances in genomic development help predict nearly all variations of pathogens thus making advances for vaccines 65 Protein based vaccines are being developed to combat resistant pathogens such as Staphylococcus and Chlamydia 64 Countering bioterrorism edit In 2005 the sequence of the 1918 Spanish influenza was completed Accompanied with phylogenetic analysis it was possible to supply a detailed account of the virus evolution and behavior in particular its adaptation to humans 66 Following the sequencing of the Spanish influenza the pathogen was also reconstructed When inserted into mice the pathogen proved to be incredibly deadly 67 12 The 2001 anthrax attacks shed light on the possibility of bioterrorism as being more of a real than imagined threat Bioterrorism was anticipated in the Iraq war with soldiers being inoculated for a smallpox attack 68 Using technologies and insight gained from reconstruction of the Spanish influenza it may be possible to prevent future deadly planted outbreaks of disease There is a strong ethical concern however as to whether the resurrection of old viruses is necessary and whether it does more harm than good 12 69 The best avenue for countering such threats is coordinating with organizations which provide immunizations The increased awareness and participation would greatly decrease the effectiveness of a potential epidemic An addition to this measure would be to monitor natural water reservoirs as a basis to prevent an attack or outbreak Overall communication between labs and large organizations such as Global Outbreak Alert and Response Network GOARN can lead to early detection and prevent outbreaks 63 See also editCyberbiosecurity Emerging field of computer securityReferences edit Sharma AK Dhasmana N Dubey N Kumar N Gangwal A Gupta M Singh Y March 2017 Bacterial Virulence Factors Secreted for Survival Indian Journal of Microbiology 57 1 1 10 doi 10 1007 s12088 016 0625 1 PMC 5243249 PMID 28148975 How Pathogens Cause Disease Microbiology courses lumenlearning com Retrieved 4 November 2019 a b Yang J Chen L Sun L Yu J Jin Q January 2008 VFDB 2008 release an enhanced web based resource for comparative pathogenomics Nucleic Acids Research 36 Database issue D539 42 doi 10 1093 nar gkm951 PMC 2238871 PMID 17984080 Gwinn M MacCannell D Armstrong GL March 2019 Next Generation Sequencing of Infectious Pathogens JAMA 321 9 893 894 doi 10 1001 jama 2018 21669 PMC 6682455 PMID 30763433 Threats Institute of Medicine US Forum on Microbial 2013 Workshop Overview National Academies Press US Retrieved 8 November 2019 Ekundayo TC Okoh AI 2018 Plesiomonas shigelloides That Were Deemed Inconclusive by Traditional Experimental Approaches Frontiers in Microbiology 9 3077 doi 10 3389 fmicb 2018 03077 PMC 6309461 PMID 30627119 Threats Institute of Medicine US Forum on Microbial 2013 Workshop Overview National Academies Press US Retrieved 8 November 2019 a b c Lynch T Petkau A Knox N Graham M Van Domselaar G October 2016 A Primer on Infectious Disease Bacterial Genomics Clinical Microbiology Reviews 29 4 881 913 doi 10 1128 CMR 00001 16 PMC 5010755 PMID 28590251 a b c d e f g h i j k Demuth A Aharonowitz Y Bachmann TT Blum Oehler G Buchrieser C Covacci A et al May 2008 Pathogenomics an updated European Research Agenda Infection Genetics and Evolution 8 3 386 93 doi 10 1016 j meegid 2008 01 005 hdl 10033 30395 PMID 18321793 a b c d Amoutzias Grigorios D Nikolaidis Marios Hesketh Andrew 17 May 2022 The Notable Achievements and the Prospects of Bacterial Pathogen Genomics Microorganisms 10 5 1040 doi 10 3390 microorganisms10051040 ISSN 2076 2607 PMC 9148168 PMID 35630482 Vinatzer BA Heath LS Almohri HM Stulberg MJ Lowe C Li S 15 May 2019 Cyberbiosecurity Challenges of Pathogen Genome Databases Frontiers in Bioengineering and Biotechnology 7 106 doi 10 3389 fbioe 2019 00106 PMC 6529814 PMID 31157218 a b c Kaiser J October 2005 Virology Resurrected influenza virus yields secrets of deadly 1918 pandemic Science 310 5745 28 9 doi 10 1126 science 310 5745 28 PMID 16210501 S2CID 26252589 a b c d e f g h i Pallen MJ Wren BW October 2007 Bacterial pathogenomics Nature 449 7164 835 42 Bibcode 2007Natur 449 835P doi 10 1038 nature06248 PMID 17943120 S2CID 4313623 a b Brownlee GG 19 August 2015 Frederick Sanger CBE CH OM 13 August 1918 19 November 2013 Biographical Memoirs of Fellows of the Royal Society 61 437 466 doi 10 1098 rsbm 2015 0013 a b c d Willey JM 2020 Prescott s microbiology New York New York McGraw Hill Education pp 431 432 ISBN 9781260211887 OCLC 1039422993 Timeline Organisms that have had their genomes sequenced Your Genome 19 January 2015 Retrieved 9 November 2019 Fleischmann RD Adams MD White O Clayton RA Kirkness EF Kerlavage AR et al July 1995 Whole genome random sequencing and assembly of Haemophilus influenzae Rd Science 269 5223 496 512 Bibcode 1995Sci 269 496F doi 10 1126 science 7542800 PMID 7542800 Key Differences between next generation sequencing and Sanger sequencing a b c d e f g h Fraser Liggett CM December 2005 Insights on biology and evolution from microbial genome sequencing Genome Research 15 12 1603 10 doi 10 1101 gr 3724205 PMID 16339357 Oakeson KF Wagner JM Mendenhall M Rohrwasser A Atkinson Dunn R September 2017 Bioinformatic Analyses of Whole Genome Sequence Data in a Public Health Laboratory Emerging Infectious Diseases 23 9 1441 1445 doi 10 3201 eid2309 170416 PMC 5572866 PMID 28820135 a b Lathe W Williams J Mangan M Karolchik D 2008 Genomic data resources challenges and promises Nature Education p 2 Davis James J Wattam Alice R Aziz Ramy K Brettin Thomas Butler Ralph Butler Rory M Chlenski Philippe Conrad Neal Dickerman Allan Dietrich Emily M Gabbard Joseph L 8 January 2020 The PATRIC Bioinformatics Resource Center expanding data and analysis capabilities Nucleic Acids Research 48 D1 D606 D612 doi 10 1093 nar gkz943 ISSN 1362 4962 PMC 7145515 PMID 31667520 Argimon Silvia Yeats Corin A Goater Richard J Abudahab Khalil Taylor Benjamin Underwood Anthony Sanchez Buso Leonor Wong Vanessa K Dyson Zoe A Nair Satheesh Park Se Eun 17 May 2021 A global resource for genomic predictions of antimicrobial resistance and surveillance of Salmonella Typhi at pathogenwatch Nature Communications 12 1 2879 doi 10 1038 s41467 021 23091 2 ISSN 2041 1723 PMC 8128892 PMID 34001879 VFDB Virulence Factors of Bacterial Pathogens www mgc ac cn Retrieved 8 November 2019 Sayers Samantha Li Li Ong Edison Deng Shunzhou Fu Guanghua Lin Yu Yang Brian Zhang Shelley Fa Zhenzong Zhao Bin Xiang Zuoshuang 8 January 2019 Victors a web based knowledge base of virulence factors in human and animal pathogens Nucleic Acids Research 47 D1 D693 D700 doi 10 1093 nar gky999 ISSN 1362 4962 PMC 6324020 PMID 30365026 Rappuoli R March 2001 Reverse vaccinology a genome based approach to vaccine development Vaccine 19 17 19 2688 91 doi 10 1016 S0264 410X 00 00554 5 PMID 11257410 Gene flow genetics Encyclopedia Britannica Retrieved 4 November 2019 Griffiths AJ Miller JH Suzuki DT Lewontin RC Gelbart WM 2000 Sources of variation An Introduction to Genetic Analysis 7th ed W H Freeman ISBN 978 0 7167 3771 1 Comparative Genomics Fact Sheet Genome gov Retrieved 13 November 2019 a b c Hain T Chatterjee SS Ghai R Kuenne CT Billion A Steinweg C et al November 2007 Pathogenomics of Listeria spp International Journal of Medical Microbiology 297 7 8 541 57 doi 10 1016 j ijmm 2007 03 016 PMID 17482873 a b Perna NT Plunkett G Burland V Mau B Glasner JD Rose DJ et al January 2001 Genome sequence of enterohaemorrhagic Escherichia coli O157 H7 Nature 409 6819 529 33 Bibcode 2001Natur 409 529P doi 10 1038 35054089 PMID 11206551 a b Koskiniemi S Sun S Berg OG Andersson DI June 2012 Selection driven gene loss in bacteria PLOS Genetics 8 6 e1002787 doi 10 1371 journal pgen 1002787 PMC 3386194 PMID 22761588 a b c d e f Bliven KA Maurelli AT December 2012 Antivirulence genes insights into pathogen evolution through gene loss Infection and Immunity 80 12 4061 70 doi 10 1128 iai 00740 12 PMC 3497401 PMID 23045475 Ward PN Holden MT Leigh JA Lennard N Bignell A Barron A et al January 2009 Evidence for niche adaptation in the genome of the bovine pathogen Streptococcus uberis BMC Genomics 10 54 doi 10 1186 1471 2164 10 54 PMC 2657157 PMID 19175920 Parkhill J Dougan G James KD Thomson NR Pickard D Wain J et al October 2001 Complete genome sequence of a multiple drug resistant Salmonella enterica serovar Typhi CT18 Nature 413 6858 848 52 Bibcode 2001Natur 413 848P doi 10 1038 35101607 PMID 11677608 Boucher Y Douady CJ Papke RT Walsh DA Boudreau ME Nesbo CL et al 2003 Lateral gene transfer and the origins of prokaryotic groups Annual Review of Genetics 37 283 328 doi 10 1146 annurev genet 37 050503 084247 PMID 14616063 Lima WC Paquola AC Varani AM Van Sluys MA Menck CF April 2008 Laterally transferred genomic islands in Xanthomonadales related to pathogenicity and primary metabolism FEMS Microbiology Letters 281 1 87 97 doi 10 1111 j 1574 6968 2008 01083 x PMID 18318843 Gill SR Fouts DE Archer GL Mongodin EF Deboy RT Ravel J et al April 2005 Insights on evolution of virulence and resistance from the complete genome analysis of an early methicillin resistant Staphylococcus aureus strain and a biofilm producing methicillin resistant Staphylococcus epidermidis strain Journal of Bacteriology 187 7 2426 38 doi 10 1128 JB 187 7 2426 2438 2005 PMC 1065214 PMID 15774886 Bapteste E Boucher Y May 2008 Lateral gene transfer challenges principles of microbial systematics Trends in Microbiology 16 5 200 7 doi 10 1016 j tim 2008 02 005 PMID 18420414 Huang J Gogarten JP July 2006 Ancient horizontal gene transfer can benefit phylogenetic reconstruction Trends in Genetics 22 7 361 6 doi 10 1016 j tig 2006 05 004 PMID 16730850 Mira A Ochman H Moran NA October 2001 Deletional bias and the evolution of bacterial genomes Trends in Genetics 17 10 589 96 doi 10 1016 S0168 9525 01 02447 7 PMID 11585665 Parkhill J Wren BW Thomson NR Titball RW Holden MT Prentice MB et al October 2001 Genome sequence of Yersinia pestis the causative agent of plague Nature 413 6855 523 7 Bibcode 2001Natur 413 523P doi 10 1038 35097083 PMID 11586360 Nierman WC DeShazer D Kim HS Tettelin H Nelson KE Feldblyum T et al September 2004 Structural flexibility in the Burkholderia mallei genome Proceedings of the National Academy of Sciences of the United States of America 101 39 14246 51 Bibcode 2004PNAS 10114246N doi 10 1073 pnas 0403306101 PMC 521142 PMID 15377793 Holden MT Titball RW Peacock SJ Cerdeno Tarraga AM Atkins T Crossman LC et al September 2004 Genomic plasticity of the causative agent of melioidosis Burkholderia pseudomallei Proceedings of the National Academy of Sciences of the United States of America 101 39 14240 5 doi 10 1073 pnas 0403302101 PMC 521101 PMID 15377794 a b What are single nucleotide polymorphisms SNPs Genetics Home Reference Retrieved 8 November 2019 a b c d e f Tettelin H Masignani V Cieslewicz MJ Donati C Medini D Ward NL et al September 2005 Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae implications for the microbial pan genome Proceedings of the National Academy of Sciences of the United States of America 102 39 13950 5 Bibcode 2005PNAS 10213950T doi 10 1073 pnas 0506758102 PMC 1216834 PMID 16172379 Lapierre P Gogarten JP March 2009 Estimating the size of the bacterial pan genome Trends in Genetics 25 3 107 10 doi 10 1016 j tig 2008 12 004 PMID 19168257 a b c Medini D Donati C Tettelin H Masignani V Rappuoli R December 2005 The microbial pan genome Current Opinion in Genetics amp Development 15 6 589 94 doi 10 1016 j gde 2005 09 006 PMID 16185861 Tettelin H Riley D Cattuto C Medini D October 2008 Comparative genomics the bacterial pan genome Current Opinion in Microbiology 11 5 472 7 doi 10 1016 j mib 2008 09 006 PMID 19086349 a b Gennari M Ghidini V Caburlotto G Lleo MM December 2012 Virulence genes and pathogenicity islands in environmental Vibrio strains nonpathogenic to humans FEMS Microbiology Ecology 82 3 563 73 doi 10 1111 j 1574 6941 2012 01427 x PMID 22676367 Langille MG Brinkman FS March 2009 IslandViewer an integrated interface for computational identification and visualization of genomic islands Bioinformatics 25 5 664 5 doi 10 1093 bioinformatics btp030 PMC 2647836 PMID 19151094 Guy L October 2006 Identification and characterization of pathogenicity and other genomic islands using base composition analyses Future Microbiology 1 3 309 16 doi 10 2217 17460913 1 3 309 PMID 17661643 Research on microbial biofilms PA 03 047 NIH National Heart Lung and Blood Institute 20 December 2002 Valle J Vergara Irigaray M Merino N Penades JR Lasa I April 2007 sigmaB regulates IS256 mediated Staphylococcus aureus biofilm phenotypic variation Journal of Bacteriology 189 7 2886 96 doi 10 1128 JB 01767 06 PMC 1855799 PMID 17277051 Hogardt M Hoboth C Schmoldt S Henke C Bader L Heesemann J January 2007 Stage specific adaptation of hypermutable Pseudomonas aeruginosa isolates during chronic pulmonary infection in patients with cystic fibrosis The Journal of Infectious Diseases 195 1 70 80 doi 10 1086 509821 PMID 17152010 a b Cheng LW Viala JP Stuurman N Wiedemann U Vale RD Portnoy DA September 2005 Use of RNA interference in Drosophila S2 cells to identify host pathways controlling compartmentalization of an intracellular pathogen Proceedings of the National Academy of Sciences of the United States of America 102 38 13646 51 Bibcode 2005PNAS 10213646C doi 10 1073 pnas 0506461102 PMC 1224656 PMID 16157870 Hattori M Taylor TD February 2009 The human intestinal microbiome a new frontier of human biology DNA Research 16 1 1 12 doi 10 1093 dnares dsn033 PMC 2646358 PMID 19147530 Hooper LV Gordon JI May 2001 Commensal host bacterial relationships in the gut Science 292 5519 1115 8 Bibcode 2001Sci 292 1115H doi 10 1126 science 1058709 PMID 11352068 S2CID 44645045 Oshima K Toh H Ogura Y Sasamoto H Morita H Park SH et al December 2008 Complete genome sequence and comparative analysis of the wild type commensal Escherichia coli strain SE11 isolated from a healthy adult DNA Research 15 6 375 86 doi 10 1093 dnares dsn026 PMC 2608844 PMID 18931093 Zoetendal EG Rajilic Stojanovic M de Vos WM November 2008 High throughput diversity and functionality analysis of the gastrointestinal tract microbiota Gut 57 11 1605 15 doi 10 1136 gut 2007 133603 PMID 18941009 S2CID 34347318 Achtman M Morelli G Zhu P Wirth T Diehl I Kusecek B et al December 2004 Microevolution and history of the plague bacillus Yersinia pestis Proceedings of the National Academy of Sciences of the United States of America 101 51 17837 42 Bibcode 2004PNAS 10117837A doi 10 1073 pnas 0408026101 PMC 535704 PMID 15598742 Oyston PC Dorrell N Williams K Li SR Green M Titball RW Wren BW June 2000 The response regulator PhoP is important for survival under conditions of macrophage induced stress and virulence in Yersinia pestis Infection and Immunity 68 6 3419 25 doi 10 1128 IAI 68 6 3419 3425 2000 PMC 97616 PMID 10816493 a b c d Pompe S Simon J Wiedemann PM Tannert C July 2005 Future trends and challenges in pathogenomics A Foresight study EMBO Reports 6 7 600 5 doi 10 1038 sj embor 7400472 PMC 1369123 PMID 15995675 a b Sette A Rappuoli R October 2010 Reverse vaccinology developing vaccines in the era of genomics Immunity 33 4 530 41 doi 10 1016 j immuni 2010 09 017 PMC 3320742 PMID 21029963 a b Rappuoli R October 2000 Reverse vaccinology Current Opinion in Microbiology 3 5 445 50 doi 10 1016 S1369 5274 00 00119 3 PMID 11050440 Taubenberger JK Reid AH Lourens RM Wang R Jin G Fanning TG October 2005 Characterization of the 1918 influenza virus polymerase genes Nature 437 7060 889 93 Bibcode 2005Natur 437 889T doi 10 1038 nature04230 PMID 16208372 S2CID 4405787 Tumpey TM Basler CF Aguilar PV Zeng H Solorzano A Swayne DE et al October 2005 Characterization of the reconstructed 1918 Spanish influenza pandemic virus Science 310 5745 77 80 Bibcode 2005Sci 310 77T CiteSeerX 10 1 1 418 9059 doi 10 1126 science 1119392 PMID 16210530 S2CID 14773861 Terrorism Project Center for Defense Information 20 December 2002 van Aken J January 2007 Ethics of reconstructing Spanish flu is it wise to resurrect a deadly virus Heredity 98 1 1 2 doi 10 1038 sj hdy 6800911 PMID 17035950 S2CID 32686445 Retrieved from https en wikipedia org w index php title Pathogenomics amp oldid 1188214497, wikipedia, wiki, book, books, library,

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