Marc Lenburg, Ph.D.

Associate Professor of Pathology and Laboratory Medicine
Adjunct Associate Professor, Bioinformatics Program

Contact Information

(617)414-1375
E-613B
mlenburg@bu.edu

Design, Analysis and Management of Microarray Experiments

false-color image of two-channel microarrayDNA-based microarrays offer an unprecedented opportunity to simultaneously measure the relative abundance of different sequences in complex mixtures of nucleic acids. Microarrays have widespread utility and some of the more common uses have included expression profiling, genotyping, and mapping the position of DNA binding proteins. Of these applications, expression profiling has captured the greatest interest among researchers since it offers the opportunity to determine how the expression of every gene in the genome (or some interesting subset of the genome) varies between different biological conditions. This has resulted in two types of microarray results: the identification of new genes involved in biological processes, and the ability to discern subtle differences and similarities between conditions. Both types of data have provided mechanistic insights into disease and fundamental biology.
The Boston University Microarray Resource is an active full-service core facility that handles about 4500 array hybridizations each year. Our goal is to help researchers apply the power of microarray-based experimental techniques to their own research projects. For expression-profiling experiments, we handle everything but RNA isolation: including help with bioinformatics and statistics. My area of expertise is microarray data analysis. I am also interested in helping researchers design their experiments and am involved in managing the development and implementation of information technology solutions for managing the large volume of data that these experiments generate.
These information technology projects have been especially important for a large-scale SNP genotyping project that we have recently finished. This project has generated 150-million genotypes in just under twelve months resulting in over a terabyte of data. We developed a centralized repository for managing the data associated with this project that allows for data to be automatically extracted and used by a large number of downstream analytic applications with minimal human intervention. We have developed a second repository to manage the results of these analyses. This results repository has been designed to promote the exploration and interpretation of results, and to facillitate collaboration among researchers and clinicians with diverse expertise. We are especially excited about the approaches we have taken to incorporate information about human genetic diversity from the International HapMap Project: as these data visualization methods allow for additional fine-mapping experiments to be designed very rapidly.

volcano plot of tumor vs normal comparison (y-axis is the significance of observed changes, x-axis is the magnitude of the change). Highlighted genes in red represent genes of a particular category.Obtaining useful data from microarray-based gene-expression experiments requires that the experiments be designed carefully to minimize the effects of irrelevant sources of variation in gene expression, to maximize the utility of the analytic methods that will be used to interpret the results, and to ensure that the experimental design will provide enough power to detect biologically important changes in gene expression. We use analytic methods such as principal-components analysis for identifying global similarities and differences between samples. We then use methods such as ANOVA and non-parametric regression to identify the differentially expressed genes that account for these differences between samples — and have been actively working to make microarray data more amenable to these types of analyses. Specific areas of progress have been in data normalization, filtering, correcting for multiple hypothesis testing, and power calculations.
We also use a number of statistical methods to combine experimental gene expression data with databases that contain information about gene function to help determine the biological processes that are affected in a given experiment. A new area of research has been developing methods for mining repositories of previous gene expression measurements to uncover additional biological implications of gene expression differences.
A further area of recent work has been discovering patterns of differential gene-expression that are able to accurately predict current or future disease from gene-expression measurements obtained from clinical samples; exploring the potential clinical utility of these diagnostics as well as using them to gain fundamental insights into disease processes.

Recent publications

  1. Beane, J.E., P. Sebastiani, T.H. Whitfield, K. Steiling, Y-M. Dumas, M.E. Lenburg, A. Spira. 2008. A prediction model for diagnosing lung cancer that integrates genomic and clinical features. Cancer Prevention Research. In Press.
  2. Blick, T., E. Widodo, H. Hugo, M. Waltham, M.E. Lenburg, R.M. Neve, E Thompson. Epithelial mesenchymal transition traits in human breast cancer cell lines. Clinical and Experimental Metastasis. In Press.
  3. Sridhar, S., F. Schembri, J. Zeskind, V. Shah, A.M. Gustafson, K. Steiling, G. Liu, Y.M. Dumas, S. Zhang, J. Brody, M.E. Lenburg, A. Spira. Smoking-induced gene expression changes in the bronchial airway are reflected in nasal and buccal epithelium. BMC Genomics. 9:259.
  4. Millien, G., J. Beane, M. Lenburg, P-N. Tsao, J. Lu, A. Spira, M.I. Ramirez. 2008. Characterization of the mid-foregut transcriptome identifies genes regulated during lung bud induction. Gene Expression Patterns. 8:124-139.
  5. Zhang, X., G. Liu, M.E. Lenburg, A. Spira. 2007. Comparison of smoking-induced gene expression on Affymetrix exon and 3′-based expression arrays. Genome Informatics. 18:247-257.
  6. Tripathi, A., C. King, A. de la Morenas, K. Perry, B. Burke, G. Antoine, E. Hirsch, M. Kavanah, J. Mendez, M. Stone, N. P. Gerry, M. E. Lenburg, C. L. Rosenberg. 2007. Gene expression abnormalities in histologically normal breast epithelium of breast cancer patients. International Journal of Cancer. 122:1557-1566.
  7. Beane, J.E., P. Sebastiani, G. Liu, J.S. Brody, M.E. Lenburg, A. Spira. 2007. Reversible and Permanent effects of Tobacco Smoke Exposure on Airway Epithelial Gene Expression. Genome Biology. 8:R201.
  8. Spira, A., J. E. Beane, V. Shah, K. Steiling, G. Liu, F. Schembri, S. Gilman, Y.-M. Dumas, P. Calner, P. Sebastiani, S. Sridhar, J. Beamis, C. Lamb, T. Anderson, N. Gerry, J. Keane, M. E. Lenburg, J. S. Brody. 2007. Airway Epithelial Gene Expression in the Diagnostic Evaluation of Smokers with Suspect Lung Cancer. Nature Medicine. 13:361-366.
  9. Lenburg, M. E., A. Sinha, D. V. Faller, G. V. Denis. 2007. Tumor-specific and proliferation-specific gene expression typifies murine transgenic B cell lymphomagenesis. Journal of Biological Chemistry. 282:4803-4811.
  10. Herbert, A.*, M. E. Lenburg*, D. Ulrich, N. P. Gerry, K. Schlauch, M. F. Christman. 2007. Open-access database of candidate associations from a genome-wide SNP scan of the Framingham Heart Study. Nature Genetics. 39:135-136. (authors contributed equally).
  11. Tchkonia, T, M. Lenburg, T. Thomou, N. Giorgadze, G. Frampton, T. Pirtskhalava, A. Cartwright, M. Cartwright, J. Flanagan, I. Karagiannides, N. Gerry, R. Forse, Y. Tchoukalova, M. Jensen, C. Pothoulakis, J. Kirkland. 2007. Identification of Depot-Specific Human Fat Cell Progenitors through Distinct Expression Profiles and Developmental Gene Patterns. American Journal of Physiology – Endocrinology and Metabolism. 292:298-307.
  12. Herbert, A., N. P. Gerry, M. McQueen, I. M. Heid, A. Pfeufer, T. Illig, H.-E. Wichmann, T. Meitinger, D. Hunter, F. B. Hu, G. Colditz, A. Hinney, J. Hebebrand, K. Koberwitz, X. Zhu, R. Cooper, K. Ardlie, H. Lyon, J. Hirschhorn, N. M. Laird, M. E. Lenburg, C. Lange and M. F. Christman. 2006. A common genetic variant is associated with adult and childhood obesity. Science. 5771:279-283.
  13. Kanefsky, J., M. Lenburg, C.-M. Hai. 2006. Cholinergic Receptor and Cyclic Stretch-Mediated Inflammatory Gene Expression in Intact ASM. American Journal of Respiratory Cell and Molecular Biology. 34: 417-425.
  14. Klings, E. S., S. Safaya, A. H. Adewoye, A. Odhiambo, G. Frampton, M. Lenburg, N. Gerry, P. Sebastiani, M. H. Steinberg, and H. W. Farber. 2005. Differential Gene Expression in Pulmonary Artery Endothelial Cells Exposed to Sickle Cell Plasma. Physiological Genomics. 21:293-298.
  15. King, C., N. Guo, G. M. Frampton, N. P. Gerry, M. E. Lenburg and C. L. Rosenberg. 2005. Reliability and reproducibility of gene expression measurements using amplified RNA from laser microdissected primary breast tissue with oligonucleotide arrays. Journal of Molecular Diagnostics. 7:57-64.
  16. Carson, J. P., N. Zhiang, G. Frampton, N. P. Gerry, M. E. Lenburg and M. F. Christman. 2004. Pharmacogenomic identification of targets for adjuvant therapy with the topoisomerase poison camptothecin. Cancer Research. 64:2096-104.
  17. Lenburg, M. E., L. S. Liou, N. P. Gerry, G. M. Frampton, H. T. Cohen, M. F. Christman. 2003. Previously unidentified changes in renal cell carcinoma gene expression identified by parametric analysis of microarray data. BMC Cancer. 3:31
  18. Lenburg, M. E. and E. K. O’Shea. 2001. Genetic evidence for a morphogenetic function of the Saccharomyces cerevisiae Pho85 cyclin-dependent kinase. Genetics. 157:39-51.
  19. Lenburg, M. E. and E. K. O’Shea. 1996. Signalling phosphate starvation. Trends in Biochemical Sciences. 21:383-7.
    Anderson S. J., M. Lenburg , N. R. Landau, J.V. Garcia. 1994. The cytoplasmic domain of CD4 is sufficient for its down-regulation from the cell surface by human immunodeficiency virus type 1 Nef. Journal of Virology 68:3092-101.
  20. Aiken, C., J. Konner, N. Landau, M. Lenburg, D. Trono. 1994. Nef induces CD4 endocytosis: requirement for a critical di-leucine motif in the membrane proximal CD4 cytoplasmic domain. Cell. 76(5):853-64.
  21. Lenburg, M. E., N. R. Landau. 1993. Vpu-induced degradation of CD4: requirements for specific amino acid residues in the cytoplasmic domain of CD4. Journal of Virology. 67:7238-7245.
  22. Snyder, R.W., M. E. Lenburg, A. T. Seebaum, L. B. Grabel. 1992. Disruption of the cytoskeleton-extracellular matrix linkage promotes the accumulation of plasminogen activators in F9 derived parietal endoderm. Differentiation. 50:153-162.
Primary teaching affiliate
of BU School of Medicine