Current research Interests
We are presently in an omics revolution in which genomes and other omes can be readily characterized, and new omics technologies can be applied to understand fundamental biology and improve human health. Our laboratory has invented many technologies to analyze genomes, transcriptomes and other omes.
We have applied these to:
1 ) Study fundamental principles of regulatory networks
2) Analyze human variation i.e. what makes people different from one another
3) Perform deep omics profiling/big data collection on individuals over time to understand what keeps them healthy and what happens when they become ill or undergo other sorts of changes (e.g. diet, etc)
4) Solve mystery diseases and disease prognosis.
Regulatory Networks and Noncoding Regions of the Human Genome
We have developed methods for mapping transcription factor binding sites through the genome. We used these approaches to develop regulatory maps and help decipher the combinatorial regulatory code – which factors work together to regulate which genes. Using this approach we are mapping pathways crucial for cell differentiation using iPSCells and for understanding regulatory networks. We are also analyzing the role of regulatory sequences in metabolism, cancer inflammation and autism.
We have been analyzing differences between individuals and species. We developed paired end sequencing for humans and found that humans have extensive structural variation (SV), i.e. deletions, insertions and inversions. This has now been shown to be a major cause of phenotypic variation and human disease. In addition, by mapping binding sites difference among tissues and cells of different humans, we have found that individuals differ much more in their regulatory information than in coding sequence differences.
We can correlate these differences with those in SNPS and SVs, thereby associating noncoding DNA differences with regulatory information. We are presently expanding this analysis to examine differences in other types of molecules: transcriptomes, proteomes, metabolomes, immunomes, microbiome, etc.
Personal Omics Profiling, Wearables and Human Disease
Our understanding of human health and what happens when people transition to disease is quite limited and poorly understood on a personal level. We have set up integrated Personal Omics Profiling (iPOP) in which we determine the genome sequences of people to predict disease risk and also analyze their transcriptome and proteome, metabolome, cytokines, immune cells and molecules in blood and/or urine in unprecedented detail over time. In this manner we determine people’s healthy state and what happens when they undergo a illness (e.g. viral infection) or other changes (diet, colonoscopy, exercise, weight gain or loss), all at a personal level.
By following a cohort of over 100 individuals we have found novel biological pathways and relationships that that occur during these periods as well as valuable information that helps manage the health of individuals.
Recently we have also pioneered the use of wearable devices for continuous and frequent monitoring of physiological parameter and shown that these can be used to:
1) detect early signs of inflammatory diseases (colds and Lyme disease)
2) distinguish insulin resistance (which is associated with Type 2 Diabetes
3) decrease blood oxygen and fatigue on aircraft.
Solving Underlying Genetic and Epigenetic Causes of Human Disease
We have established pipelines for mapping genomes and identifying genetic mutations underlying human disease. Using these pipelines we have solved a number of undiagnosed diseases including those involved in immunology (TT7, NFKappaB), developmental delay (NGLY1), inflammatory bowel disease (HSP1LA) and others. We are also examining the role of epigenetics (DNA Methylation, Chromatin) in human diseases such as autism and cancer.
Examples of Key Publications:
Large-scale analysis of gene expression, protein localization, and gene disruption in Saccharomyces cerevisiae.
Burns N, Grimwade B, Ross-Macdonald PB, Choi EY, Finberg K, Roeder GS and Snyder M. Genes Dev. 1994;8:1087-105.
Large-scale analysis of the yeast genome by transposon tagging and gene disruption.
Ross-Macdonald P, Coelho PSR, Roemer T, Agarwal S, Kumar A, Jansen R, Cheung K-H, Sheehan A, Symoniatis D, Umansky L, Heitman M, Nelson FK, Iwasaki H, Hager K, Gerstein M, Miller P, Roeder GS, Snyder M. Nature. 1999; 402: 413-418.
Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF
Iyer VR, Horak CE, Scafe CS, Botstein D, Snyder M and Brown PO. . Nature. 2001;409:533-8.
Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing
Robertson G, Hirst M, Bainbridge M, Bilenky M, Zhao Y, Zeng T, Euskirchen G, Bernier B, Varhol R, Delaney A, Thiessen N, …, Snyder M and Jones S. Nat Methods. 2007;4:651-7.
GATA-1 binding sites mapped in the beta-globin locus by using mammalian ChIP-chip analysis.
Horak CE, Mahajan MC, Luscombe NM, Gerstein M, Weissman SM, Snyder M. Proc Natl Acad Sci USA. 2002;99: 2924-29.
The ENCODE Project Consortium. “An Integrated Encyclopedia of DNA Elements in the Human Genome.
Nature. 2012. 489(7414): 57-74.
The transcriptional activity of human Chromosome 22
Rinn JL, Euskirchen G, Bertone P, Martone R, Luscombe NM, Hartman S, Harrison PM, Nelson FK, Miller P, Gerstein M, Weissman S and Snyder M. Genes Dev. 2003;17:529-40.
Global identification of human transcribed sequences with genome tiling arrays.
Bertone P, Stolc V, Royce TE, Rozowsky JS, Urban AE, Zhu X, Rinn JL, Tongprasit W, Samanta M, Weissman S, Gerstein M, Snyder M. Science. 2004;306: 2242-6.
The transcriptional landscape of the yeast genome defined by RNA sequencing.
Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M and Snyder M. Science. 2008;320:1344-9.
RNA-Seq: a revolutionary tool for transcriptomics.
Wang Z, Gerstein M, Snyder M. Nat Rev Genet. 2009 Jan;10(1):57-63. PMID: 19015660.
New insights into Acinetobacter baumannii pathogenesis revealed by high-density pyrosequencing and transposon mutagenesis.
Smith MG, Gianoulis TA, Pukatzki S, Mekalanos J, Ornston LN, Gerstein M, Snyder M. Genes Dev. 2007;21: 601-14.
Paired-end mapping reveals extensive structural variation in the human genome.
Korbel JO,* Urban AE,* Affourtit J,* Godwin B, Grubert F, ... Snyder M. Science. 2007;318: 420-6.
Global analysis of protein activities using proteome chips.
Zhu H, Bilgin M, Bangham R, Hall D, Casamayor A, Bertone P, Lan N, Jansen R, Bidlingmaier S, Houfek T, Mitchell T, Miller P, Dean DA, Gerstein M, Snyder M. Science. 2001;293: 2101-2105.
Analysis of yeast protein kinases using protein chips.
Zhu H, Klemic JF, Chang S, Bertone P, Klemic KG, Smith D, Gerstein M, Reed MA, Snyder M. Nat Genet. 2000;26: 283-289.
Variation in transcription factor binding among humans.
Kasowski M, Grubert F, Heffelfinger C, Hariharan M, Asabere A, Waszak SM, Habegger L, Rozowsky J, Shi M, Urban AE, … Weissman SM, Gerstein MB, Korbel JO, Snyder M. Science. 2010. 328(5975): 232-5. Epub 2010. PMID: 20299548.
Divergence of transcription factor binding sites across related yeast species.
Borneman AR, Gianoulis TA, Zhang ZD, Yu H, Rozowsky J, Seringhaus MR, Wang LY, Gerstein M, Snyder M. Science. 2007;317: 815-19.
Genetic Control of Chromatin States in Humans Involves Local and Distal Chromosomal Interactions.
Grubert F, Zaugg JB, Kasowski M, Ursu O, Spacek DV, Martin AR, Greenside P, Srivas R, Phanstiel DH, Pekowska A, Heidari N, Euskirchen G, Huber W, Pritchard JK, Bustamante CD, Steinmetz LM, Kundaje A, Snyder M.Cell. 2015 Aug 27;162(5):1051-65. doi: 10.1016/j.cell.2015.07.048. Epub 2015 Aug 20. PMID: 26300125
Extensive variation in chromatin states across humans.
Kasowski M, Kyriazopoulou-Panagiotopoulou S, Grubert F, Zaugg JB, Kundaje A, Liu Y, Boyle AP, Zhang QC, Zakharia F, Spacek DV, Li J, Xie D, Olarerin-George A, Steinmetz LM, Hogenesch JB, Kellis M, Batzoglou S, Snyder M.Science. 2013 Nov 8;342(6159):750-2. PMID: 24136358
Personal omics profiling reveals dynamic molecular and medical phenotypes.
Chen R, Mias GI, Li-Pook-Than J, Jiang L, … Snyder M. Cell. 2012;148:1293-307.
Digital Health: Tracking Physiomes and Activity Using Wearable Biosensors Reveals Useful Health-Related Information.
Li X, Dunn J, Salins D, Zhou G, Zhou W, Schüssler-Fiorenza Rose SM, Perelman D, Colbert E, Runge R, Rego S, Sonecha R, Datta S, McLaughlin T, Snyder MP.PLoS Biol. 2017 Jan 12;15(1):e2001402. doi: 10.1371/journal.pbio.2001402. PMID: 28081144