Data-mining Method Unmasks Pathogenic Sequences Lurking in Human DNA
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Over the past decade, it has become clear that microorganisms are vastly more diverse and ubiquitous than our egocentric species ever thought possible. This has led some scientists to suspect that microbes may be causing human diseases we currently blame on poorly understood genetic or lifestyle factors. Indeed, hepatitis, Kaposi sarcoma, Whipple syndrome and some other diseases are now known to have viral or bacterial origins, while others, including multiple sclerosis and some cases of heart disease, are suspected of being in that category (Relman et al 1992, Chang, et al, 1994, Ascherio A et al. 2001).
How about Alzheimer's disease? Some researchers believe that herpes simplex virus infection can exacerbate AD in some people (see AD hypothesis), but no one has come up with a compelling microbial connection.
A paper in the January 14 online edition of Nature Genetics presents a computational method to tackle this question in an unbiased way. Researchers working with Harvard's Matthew Meyerson and George Church developed a way of filtering viral, bacterial, fungal, and protozoan sequences out of human expressed sequence tag (EST) libraries.
This in-silico method works much like subtractive hybridization, in which single-strand DNAs from different libraries are physically mixed together so that matching sequences can pair up and the differences between the libraries become apparent because they remain single-stranded.
Here, the scientists used the draft sequence of the human genome as a reference against which they compared various human EST libraries. All sequences that matched human sequences were subtracted, as were those belonging to cloning vectors and other known contaminants. That plus some quality filtering left a detritus of two percent of the original "human" sequences, which turned out to contain known pathogenic and commensal microorganisms, including hepatitis B and C viruses, Epstein-Barr virus, and other, unknown sequences.
In an intriguing experiment, Meyerson and colleagues found 18 sequences matching hepatitis B virus in a library of 16,743 sequences prepared from noncancerous liver tissue from a patient with liver cancer. And a feasibility experiment detected two human papilloma virus (HPV-18) sequences in a library containing 7,073 sequences that was prepared from cervical carcinoma cells harboring HPV-18, suggesting huge libraries (and therefore impractical amounts of sequencing) may not always be necessary to detect infectious agents.
Meyerson says applying this to Alzheimer's would require the generation of cDNA libraries from postmortem neurons of a few well-characterized AD patients whose history suggests a prior infection. Then, transcript filtering against the human genome could try to establish a correlation with an infectious agent.—Gabrielle Strobel
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Papers
- Relman DA, Schmidt TM, MacDermott RP, Falkow S. Identification of the uncultured bacillus of Whipple's disease. N Engl J Med. 1992 Jul 30;327(5):293-301. PubMed.
- Chang Y, Cesarman E, Pessin MS, Lee F, Culpepper J, Knowles DM, Moore PS. Identification of herpesvirus-like DNA sequences in AIDS-associated Kaposi's sarcoma. Science. 1994 Dec 16;266(5192):1865-9. PubMed.
- Ascherio A, Munger KL, Lennette ET, Spiegelman D, HernĂ¡n MA, Olek MJ, Hankinson SE, Hunter DJ. Epstein-Barr virus antibodies and risk of multiple sclerosis: a prospective study. JAMA. 2001 Dec 26;286(24):3083-8. PubMed.
Primary Papers
- Weber G, Shendure J, Tanenbaum DM, Church GM, Meyerson M. Identification of foreign gene sequences by transcript filtering against the human genome. Nat Genet. 2002 Feb;30(2):141-2. PubMed.
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