Download NIH Biosketch - Carlo Colantuoni

OMB No. 0925-0001/0002 (Rev. 08/12 Approved Through 8/31/2015)
BIOGRAPHICAL SKETCH
NAME: Carlo Colantuoni
eRA COMMONS USER NAME: CCOLANT2
POSITION TITLE: Lead Bioinformatics Software Engineer, Inst. for Genome Sciences, Univ. of Maryland SOM
and Adjunct Assistant Professor, Depts. of Neurology and Neuroscience, Johns Hopkins Univ. SOM
EDUCATION/TRAINING
DEGREE
Completion
Date
AB
06/1996
Biological Psychology
PhD
10/2001
Neuroscience
Johns Hopkins School of Public Health
PostDoc
9/2003
Bioinfoirmatics
National Institute of Mental Health, NIH
PostDoc
11/2006
INSTITUTION AND LOCATION
Princeton University
Johns Hopkins School of Medicine
FIELD OF STUDY
Neuropsychiatric Genomics
A. Personal Statement
My research aims to elucidate cellular mechanisms that connect genotype and phenotype in normal human
brain development and neuropsychiatric disease risk by integrating diverse sources of genomic data derived
from dynamic cellular and tissue systems. We have focused a great deal of our attention on data from
pluripotent cells differentiating in vitro. Using novel data decomposition methods, we have achieved an
unprecedented level of resolution in this system, connecting transcriptional signatures of individual genomes
with specific cellular behavior in early neural development. Using lineage-specific temporal patterns of
expression from this cellular system as a “genomic lens”, we are able to dissect signatures from in vivo human
brain transcriptional data, and in this manner engage powerful in vitro cellular systems in order to investigate
basic mechanisms of in vivo human brain development.
1] Fertig EJ, Stein-O'Brien G, Jaffe A, Colantuoni C. Pattern Identification in Time-Course Gene
Expression Data with the CoGAPS Matrix Factorization. Methods Mol Biol. 2014;1101:87-112. doi:
10.1007/978-1-62703-721-1_6.
2] Colantuoni C, Lipska BK, Ye T, Hyde TM, Tao R, Leek JT, Colantuoni EA, Elkahloun AG, Herman MM,
Weinberger DR, Kleinman JE. Temporal dynamics and genetic control of transcription in the human
prefrontal cortex. Nature. 2011 Oct 26;478(7370):519-23. doi: 10.1038/nature10524.
B. Positions and Honors
1996: Elected member of Sigma Xi, Scientific Research Society
2003-2005: Visiting Scholar, Department of Biostatistics, Johns Hopkins School of Public Health
2006-2011: Associate, Department of Biostatistics, Johns Hopkins School of Public Health
2006-2011: Founder/Owner, Illuminato Biotechnology, Inc., Neuroscience Bioinformatics Consulting
2011-2016: Investigator, Lieber Institute for Brain Development, Genome Informatics
C. Contribution to Science
1] Genome Dynamics in Human Brain Development: In 2001, we published one of the first studies of highthroughput gene expression analysis in postmortem human brain tissue. This transcriptional portrait of the
neocortex in the severe neurodevelopmental disorder, Rett Syndrome, was the first molecular definition of
known neuropathology through genomics methods. In my more recent work, I have focused on delineating
genome-wide dynamics in normal human brain development. In particular, we described waves of
transcriptional change in pre-natal development that are reversed just after birth and which are intertwined with
changes in gene expression decades later in life. This notion that many early processes in neurodevelopment
are transient as part of highly non-linear transitions fundamental to states much later in mature tissue has been
studied at many levels by others, and remains central to my work. The creation of public data resources
enabling the further exploration of these landscapes of normal human brain development has been a focus:
http://www.libd.org/braincloud, and http://braincloud.jhmi.edu/plots/.
A] Colantuoni C, Lipska BK, Ye T, Hyde TM, Tao R, Leek JT, Colantuoni EA, Elkahloun AG, Herman MM,
Weinberger DR, Kleinman JE. Temporal dynamics and genetic control of transcription in the human
prefrontal cortex. Nature. 2011 Oct 26;478(7370):519-23. doi: 10.1038/nature10524.
B] Numata S, Ye T, Hyde TM, Guitart-Navarro X, Tao R, Wininger M, Colantuoni C, Weinberger DR,
Kleinman JE, Lipska BK. DNA methylation signatures in development and aging of the human prefrontal
cortex. Am J Hum Genet. 2012 Feb 10;90(2):260-72.
C] Colantuoni C, Hyde TM, Mitkus S, Joseph A, Sartorius L, Aguirre C, Creswell J, Johnson E, DeepSoboslay A, Herman MM, Lipska BK, Weinberger DR, Kleinman JE. Age-related changes in the
expression of schizophrenia susceptibility genes in the human prefrontal cortex. Brain Struct Funct. 2008
May 10.
D] Colantuoni C, Jeon OH, Hyder K, Chenchik A, Khimani AH, Narayanan V, Hoffman EP, Kaufmann
WE, Naidu S, Pevsner J. Gene expression profiling in postmortem Rett Syndrome brain: differential gene
expression and patient classification. Neurobiol Dis. 2001 Oct;8(5):847-65.
2] Computational Methods in Genomics: Throughout my career exploring human brain development with
diverse genome-wide methods, I have invested effort in developing new computational approaches. Before
either open-source or commercial software for the processing of microarray data were available, we developed
algorithms for expression data using a loess normalization that is still widely used (eg. limma in Bioconductor,
and at: http://pevsnerlab.kennedykrieger.org/snomadinput.html). Most recently, we have worked to develop
novel methods for the high-resolution decomposition of temporal dynamics in genome-wide data, and the
functional interrogation of these dynamics across diverse biological systems. As the amount of data centered
on neural differentiation grows exponentially, I have dedicated efforts to dissect individual datasets at greater
depth and to functionally understand each of these in the context of many other related data sets.
A] Fertig EJ, Stein-O'Brien G, Jaffe A, Colantuoni C. Pattern Identification in Time-Course Gene
Expression Data with the CoGAPS Matrix Factorization. Methods Mol Biol. 2014;1101:87-112. doi:
10.1007/978-1-62703-721-1_6.
B] Jaffe AE, Hyde T, Kleinman J, Weinberger DR, Chenoweth JG, McKay RD, Leek JT, Colantuoni C.
Practical impacts of genomic data "cleaning" on biological discovery using surrogate variable analysis.
BMC Bioinformatics. 2015 Nov 6;16(1):372. PubMed PMID: 26545828.
C] Colantuoni C, Henry G, Zeger S, Pevsner J. SNOMAD (Standardization and NOrmalization of
MicroArray Data): web-accessible gene expression data analysis. Bioinformatics. 2002 Nov;18(11):1540-1.
D] Colantuoni C, Henry G, Zeger S, Pevsner J. Local mean normalization of microarray element signal
intensities across an array surface: quality control and correction of spatially systematic artifacts.
Biotechniques. 2002 Jun;32(6):1316-20.
3] Schizophrenia Genomics: In collaborative work exploring the genomics of schizophrenia, I have applied
many of the computational methods I developed to data from diverse cellular systems, and patient derived
tissues.
A] Kondo MA, Tajinda K, Colantuoni C, Hiyama H, Seshadri S, Huang B, Pou S, Furukori K, Hookway C,
Jaaro-Peled H, Kano SI, Matsuoka N, Harada K, Ni K, Pevsner J, Sawa A. Unique pharmacological actions
of atypical neuroleptic quetiapine: possible role in cell cycle/fate control. Transl Psychiatry. 2013 Apr
2;3:e243. doi: 10.1038/tp.2013.19.
B] Mor E, Kano SI, Colantuoni C, Sawa A, Navon R, Shomron N. MicroRNA-382 expression is elevated in
the olfactory neuroepithelium of schizophrenia patients. Neurobiol Dis. 2013 Mar 29. doi:pii: S09699961(13)00099-5. 10.1016/j.nbd.2013.03.011.
C] Kano S, Colantuoni C, Han F, Zhou Z, Yuan Q, Wilson A, Takayanagi Y, Lee Y, Rapoport J, Eaton W,
Cascella N, Ji H, Goldman D, Sawa A. Genome-wide profiling of multiple histone methylations in olfactory
cells: further implications for cellular susceptibility to oxidative stress in schizophrenia. Mol Psychiatry. 2012
Aug 28. doi: 10.1038/mp.2012.120.
D] Tajinda K, Ishizuka K, Colantuoni C, Morita M, Winicki J, Le C, Lin S, Schretlen D, Sawa A, Cascella
NG. Neuronal biomarkers from patients with mental illnesses: a novel method through nasal biopsy
combined with laser-captured microdissection. Mol Psychiatry. 2010 Mar;15(3):231-2.
4] Genomics of Cognition in Aging: Using many of the approaches I developed, I engaged in extensive
collaborative work investigating transcriptional dynamics in learning and variable cognitive outcomes in aging
among genetically diverse rodents. Combining precise behavioral paradigms and cognitive assessment of
aging outbred rats with transcriptional profiling, we were able to identify particular pathways involved in
learning which may underlie preserved cognition in aging.
A] Haberman RP, Colantuoni C, Koh MT, Gallagher M. Behaviorally activated mRNA expression profiles
produce signatures of learning and enhanced inhibition in aged rats with preserved memory. PLoS One.
2013 Dec 13;8(12):e83674. doi:10.1371/journal.pone.0083674.
B] Haberman RP, Colantuoni C, Stocker AM, Schmidt AC, Pedersen JT, Gallagher M. Prominent
hippocampal CA3 gene expression profile in neurocognitive aging. Neurobiol Aging. 2009 Nov 12.
C] Haberman RP, Lee HJ, Colantuoni C, Koh MT, Gallagher M. Rapid encoding of new information alters
the profile of plasticity-related mRNA transcripts in the hippocampal CA3 region. Proc Natl Acad Sci U S A.
2008 Jul 23.
D] Gallagher M, Colantuoni C, Eichenbaum H, Haberman RP, Rapp PR, Tanila H, Wilson IA. Individual
differences in neurocognitive aging of the medial temporal lobe. Age, Sept. 2006. DOI 10.1007/s11357006-9017-5.
Complete list of published work on My Bibliography at NCBI:
http://www.ncbi.nlm.nih.gov/sites/myncbi/1jQ_sYKsbrq/bibliography/40840800/public/?sort=date&direction=des
cending.
D. Research Support