Research Interests

• The microbiome in health and disease, bioinformatics and understanding the transcriptome of immune cells

Publications

We have projects working on the urinary microbiome in healthy women and woman with bladder, vaginal or kidney disorders. We have found that most women have Lactobacillus in their urine and that most women’s bladders have a dominant bacterial genera that accounts for >50% of the bacteria that are detected. We have also found that many bacterial genera can be present, such as Staphylococcus, but at a much lower frequency. We are currently studying the relationship between these lower frequency populations and clinical parameters.

We are also studying the ocular microbiome and its role in floppy eyelid disease, Steven’s Johnson Syndrome, graph vs. host disease and aqueous tear deficiency. The eye is another low biomass site, but we have developed robust methods to interrogate the microbiome in this body site. Technology advances are also allowing us to collect and process samples more quickly so we can spend more time on data analysis.

• Pearce, M.M., Hilt E.E., Rosenfeld, A.B., Zilliox, M.J., Thomas-White, K., Fok C., Kliethermes, S., Schreckenberger P.C., Brubaker, L., Gai, X. and Wolfe, A.J. 2014. The female urinary microbiome: a comparison of women with and without urgency urinary incontinence. MBio 8:e01283-e01284.
• Hilt, E., McKinley, K., Pearce, M.M., Rosenfeld, A.B., Zilliox, M.J., Mueller, E.R., Brubaker, L., Gai, X., Wolfe, A.J. and Schreckenberger, P. 2014. Urine is not sterile: use of enhanced urine culture techniques to detect resident bacterial flora in the adult female bladder. J. Clin. Microbiol. 52:871-876.
• Pearce, M.M.#, Zilliox, M.J.#, Rosenfeld, A.B., Thomas-White, K.J., Richter, H.E., Nager, C.W., Visco, A.G., Nygaard, I.E., Barber, M.D., Schaffer, J., Moalli, P., Sung, V.W., Smith, A.L., Rogers, R., Nolen, T.L., Wallace, D., Meikle, S.F., Gai, X., Wolfe, A.J., Brubaker, L. Pelvic Floor Disorder Network. 2015. The female urinary microbiome in urgency urinary incontinence. Am. J. Obstet. Gynecol. 213:347.e1-347.e11.
  #Co-first authors.
• Thomas-White, K.J., Hilt, E.E., Fok, C., Pearce, M.M., Mueller, E.R., Kliethermes, S., Jacobs, K., Zilliox, M.J., Brincat, C., Price, T.K., Kuffel, G., Schreckenberger, P., Gai, X., Brubaker, L., Wolfe, A.J. 2015. Incontinence medication response relates to the female urinary microbiota. Int. Urogynecol. J. [Epub ahead of print].
• Malki, K., Shapiro, J.W., Price, T.K., Hilt, E.E., Thomas-White, K., Sircar, T., Rosenfeld, A.B., Kuffel, G., Zilliox, M.J., Wolfe, A.J., Putonti, C. 2016. Genomes of gardnerella strains reveal an abundance of prophages within the bladder microbiome. PLoS One. 11:e0166757.
• Funding: P20 and R01 from NIDDK

With the completion of the human genome project, scientific effort has turned to understanding the transcriptome, epigenome and proteome. Our lab’s interest is in understanding the transcriptome of T cells in antitumor and antiviral responses. Normal gene expression analysis methods are optimized for wet-lab experiments, where researchers have replicates and want to minimize variation. During the immune response, investigators must deal with samples that have increased variation due to cellular activation. To address these issues, our gene expression methods do not look at up- and down-regulated genes, but whether genes are expressed/unexpressed in a given sample. We have recently found that 30% of regulated immune genes are missed due to these problems and we are working to understand their role in protection.

• Zilliox, M.J. and Irizarry, R.A. 2007. A gene expression bar code for microarray data. Nature Methods 4:911-913. PMCID: PMC3154617.
• McCall, M.N., Uppal, K., Jaffee, H.A., Zilliox, M.J.* and Irizarry, R.A.* 2011. The Gene Expression Barcode: leveraging public data repositories to begin cataloging the human and murine transcriptomes. Nucleic Acids Research 39:D1011-D1015. PMCID:PMC3013751.
  *Co-corresponding authors.
• Wu, G., Yustein, J.T., McCall, M.N., Zilliox, M., Irizarry, R.A., Zeller, K., Dang, C.V. and Ji, H. 2013. ChIP- PED enhances the analysis of ChIP-seq and ChIP-chip data. Bioinformatics 29:1182-1189.
• McCall, M.N., Jaffee, H.A., Zelisko, S.J., Sinha, N., Hooiveld, G., Irizarry, R.A. and Zilliox, M.J. 2014. The Gene Expression Barcode 3.0: improved data processing and mining tools. Nucleic Acids Research 42:D938- D943.
• Funding: NIH, Loyola University, University of Rochester and Harvard University Institutional Funds