MicroRNAs: Principles of Target Recognition and Developmental Roles Vikram Agarwal

248 Pages·2015·25.23 MB·English

by  Vikram Agarwal

MicroRNAs: Principles of Target Recognition and Developmental Roles
by

Vikram Agarwal

B.S. Biology (2009)
University of Texas at Austin

SUBMITTED TO THE COMPUTATIONAL AND SYSTEMS BIOLOGY
GRADUATE PROGRAM IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY

September 2015

© 2015 Vikram Agarwal. All rights reserved.

The author hereby grants to MIT permission to reproduce and to distribute publicly paper
and electronic copies of this thesis document in whole or in part in any medium now
known or hereafter created.

Signature of author………………………………………………………………….............
Vikram Agarwal
Computational and Systems Biology Program
August 28, 2015

Certified by………………………………………………………………………………....
David P. Bartel
Professor of Biology
Thesis Supervisor

Accepted by………………………………………………………………………………...
Christopher Burge
Professor of Biology and Biological Engineering
Director, Computational and Systems Biology Graduate Program
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MicroRNAs: Principles of Target Recognition and Developmental Roles

by

Vikram Agarwal

Submitted to the Computational and Systems Biology Program on August 28, 2015,
In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

Abstract

MicroRNAs (miRNAs) are ~21–24 nt non-coding RNAs that mediate the degradation
and translational repression of target mRNAs. The genomes of vertebrate organisms
encode hundreds of miRNAs, each of which may regulate hundreds of mRNA targets.
Thus, miRNAs are crucial post-transcriptional regulators engaged in vast regulatory
networks. To date, the characteristics of these networks remain mysterious due to the
difficulty of identifying miRNA targets through either experimental or computational
means. To understand the physiological roles of miRNAs in animal species, it is of
fundamental importance to elucidate the structure of the targeting networks in which they
participate.

The recognition of a miRNA target is guided largely by perfect Watson-Crick base
pairing interactions between nucleotides 2–7 from the 5′ end of the miRNA (i.e., the
“seed” region) and complementary motifs embedded in the 3′ UTRs of the target
mRNAs. The prevalence of these motifs throughout the transcriptome poses a challenge
to our understanding of how specificity emerges: since the presence of a motif is not
sufficient to mediate target repression, what contextual features discriminate effective
target sites from ineffective ones? Further complicating this is the proposition that “non-
canonical” sites lacking perfect seed pairing might mediate repression, which would
expand the potential number of functional target sites by orders of magnitude. In the
second chapter of this work, we define the features that predict effective miRNA target
sites, incorporating their relative influence into a quantitative model which can out-
perform existing computational models and experimental approaches in target
identification.

Though the molecular roles of miRNAs in gene regulation have long been appreciated,
the functions of most miRNAs in living organisms has remained elusive. In the third
chapter of this work, we discuss the consequences of genetic ablation of miR-196, a
deeply conserved miRNA that is predicted to simultaneously repress many HOX genes,
in the mouse. We propose a role for miR-196 in the spatial patterning of the vertebrate
axial skeleton. Isolating the cell populations that express the miRNA during early
mammalian development, we attempt to characterize the direct in vivo targets of miR-196
and dissect the molecular underpinnings of the phenotypes observed.

Thesis Advisor: David P. Bartel
Title: Professor of Biology
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Acknowledgments

I am indebted to my professor, David Bartel, for being an outstanding mentor and role
model during the course of my graduate work. His level of scientific rigor, enduring
patience, attention to detail, and ready willingness to offer his help and extensive feedback,
has made a lasting impression on me and will undoubtedly influence my style of scientific
inquiry throughout the course of my life.

I thank my thesis committee members, Phil Sharp and Chris Burge, for providing me
extensive feedback on my work throughout the years, and for their helpful advice on
career opportunities. I also thank Gary Ruvkun for serving as my outside committee
member. There are also many professors at MIT who taught their courses with great
passion, and their inspiring methods of teaching have greatly impacted my interests in
biology and computer science.

I am grateful the graduate students and postdocs who mentored me throughout these
years. Robin Friedman in particular was instrumental in patiently explaining the
statistical methods in phylogenomics that he developed. I have also had countless
discussions with David Garcia, Jin-Wu Nam, Alex Subtelny, Igor Ulitsky, Olivia
Rissland, and Junjie Guo that have broadened the scope of my thinking and heavily
impacted the work presented in this thesis.

I thank my scientific collaborators over the years, particularly Rémy Denzler and Markus
Stoffel, with whom I had the opportunity to explore interesting questions concerning
physiology. Rémy has also been a great friend and I have been lucky to have great fun in
our travels together. My work with Eddy McGlinn reignited my interests in exploring
developmental questions, and I thank her for giving me the opportunity to work with her
group and for helping me understand the biology and improve my communication of the
work in presentations.

The Bartel lab has been an incredible place to work and I couldn’t have asked for a more
welcoming home. Beyond colleagues, the people in the lab have been close friends and I
appreciate every member of the past and present. Inside the lab, they’ve made it a great
environment to discuss ideas openly together, and outside the lab, they’ve made
Cambridge and Boston a great town to explore together.

I thank everyone in the Computational and Systems Biology (CSB) class of 2009 (Chris,
Anna, Adrian, Zi, and Xuebing) for their continued friendship, as well as friends in the
Microbiology (Mark, Nicole, and Chris) and Biology (Josh and Brian) programs for
making my experiences in Cambridge tremendously enjoyable and memorable. I also
thank Bonnielee Whang and Jacquie Carota for their support of the CSB program.

Lastly, I thank my family for supporting me throughout my life and inspiring an interest
in exploring scientific questions early on. My brother has been greatly influential in my
work and it has been a pleasure learning from his life experiences, many of which I’ve
paralleled in mine.
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Table of Contents
Abstract ........................................................................................................................................... 3
Acknowledgements ......................................................................................................................... 5
Chapter 1. Introduction ................................................................................................................. 9
The many layers of gene regulation ............................................................................................ 9
MicroRNAs: Discovery and biological roles ................................................................................. 11
Biogenesis of microRNAs and mechanisms of targeting ............................................................... 15
Computational approaches to microRNA target prediction ........................................................... 17
Experimental approaches to microRNA target identification ........................................................ 23
References ............................................................................................................................... 28
Chapter 2. Predicting effective microRNA target sites in mammalian mRNAs ..................... 37
Abstract .................................................................................................................................... 38
Introduction ............................................................................................................................. 38
Results ..................................................................................................................................... 43
Inefficacy of recently reported non-canonical binding sites.................................................. 43
Confirmation that miRNAs bind to non-canonical sites despite their inefficacy ................... 48
Improving dataset quality for model development ............................................................... 52
Selecting features and building a regression model for target prediction .............................. 55
Improvement over previous methods ................................................................................... 59
Similar response of targets predicted from the model and the most informative CLIP
experiments .................................................................................................................. 63
The TargetScan database (v7.0) .......................................................................................... 66
Discussion ................................................................................................................................ 69
Materials and Methods ............................................................................................................ 78
Microarray, RNA-seq, and RPF dataset processing ............................................................. 78
Crosslinking and other interactome datasets ........................................................................ 80
Motif discovery for non-canonical binding sites .................................................................. 82
Microarray dataset normalization ........................................................................................ 83
RNA structure prediction .................................................................................................... 84
Calculation of P scores .................................................................................................... 85
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Selection of mRNAs for regression modeling ...................................................................... 86
Scaling the scores of each feature ........................................................................................ 87
Stepwise regression and multiple linear regression models .................................................. 88
Collection and processing of previous predictions ............................................................... 89
3′-UTR profiles for TargetScan7 predictions ....................................................................... 90
MicroRNA sets for TargetScan7 ......................................................................................... 92
TargetScan7 predictions ...................................................................................................... 93
Acknowledgements ................................................................................................................. 96
References ............................................................................................................................... 97
Figures and figure legends ................................................................................................ 105
Tables ...............................................................................................................................1 40
Chapter 3. Independent regulation of vertebral number and vertebral identity by
microRNA-196 paralogs ........................................................................................................... 143
Abstract ................................................................................................................................. 144
Introduction .......................................................................................................................... 144
Results .................................................................................................................................. 148
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Differential transcription of miR-196a1 and miR-196a2 in the developing embryo ............ 148
Genetic deletion of miR-196 leads to altered vertebral identity ............................................ 149
Genetic deletion of miR-196 leads to an increase in vertebral number ................................. 151
Transcriptome alterations are detected following allelic removal of miR-196 activity ........ 152
Hox cluster expression dynamics are altered in miR-196 mutant embryos .......................... 153
Identification of additional direct targets of miR-196 ..........................................................155
miR-196 activity is required for signaling pathways associated with axis elongation,
segmentation and the trunk-to-tail transition ........................................................................ 156
miR-196 has the potential to modulate Wnt signaling by multiple mechanisms .................. 158
Discussion ............................................................................................................................. 160
miR-196 activity is essential for vertebral identity ............................................................... 160
miR-196 activity constrains total vertebral number .............................................................. 162
Materials and Methods ......................................................................................................... 165
miR-196a1GFP and miR-196a2GFP knock-in construction ..................................................... 165
miR-196a1–/– and miR-196a2–/– and miR-196b–/– generation ................................................. 165
Mouse skeletal preparation and analysis ............................................................................... 166
In situ hybridization .............................................................................................................. 166
FACS sorting and RNA-seq sample preparation .................................................................. 166
RNA-seq and category enrichment analysis ......................................................................... 166
miRNA target analysis .......................................................................................................... 167
Permutation test for significance testing ............................................................................... 168
In vitro luciferase assay ........................................................................................................ 168
Chick electroporation and in vivo BatLuc reporter analysis ................................................. 168
Acknowledgements ............................................................................................................... 169
References ............................................................................................................................. 171
Figures and figure legends ............................................................................................... 177
Tables .............................................................................................................................. 197
Chapter 4. Future Directions ................................................................................................... 199
Quantitative models of miRNA targeting in Drosophila ..................................................... 199
Conservation of miRNA targeting networks among bilaterians ......................................... 201
References ............................................................................................................................ 203
Appendix 1. Global analysis of the effect of different cellular contexts on microRNA
targeting ..................................................................................................................................... 205
Appendix 2. Assessing the ceRNA hypothesis with quantitative measurements of miRNA
and target abundance ............................................................................................................... 219
Appendix 3. Expanded identification and characterization of mammalian circular
RNAs .......................................................................................................................................... 231
Curriculum Vitae ......................................................................................................................... 247
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Chapter 1. Introduction

The many layers of gene regulation
It is a remarkable experience to marvel at the diversity of forms among the organisms
inhabiting our planet. Plants and animals exhibit a wide range of shapes, sizes, and
behaviors; they have adapted to most habitats, conquering the seas, lands, and skies. It is
likely that the morphological diversity that is observed throughout life is largely a result
of two evolutionary processes: the birth of genes and acquisition of novel gene function
(Kaessmann, 2010; Tautz and Domazet-Loso, 2011; Carvunis et al., 2012) as well as
gene regulatory innovation (Wray, 2007; Carroll, 2008). While gene innovation may have
played a greater role early in evolutionary time (i.e., between 3–3.5 billion years ago)
(David and Alm, 2011), organismal complexity in higher eukaryotes may have instead
arisen from the sophisticated regulation of gene expression (Levine and Tjian, 2003).
The central dogma of molecular biology details the predominant mode of
information flow in cells: genes are encoded in DNA, transcribed into messenger RNAs
(mRNAs), and these mRNAs are translated into proteins (Crick, 1970). A large body of
evidence suggests that every step of this process appears to be intricately regulated, and
the cell has exploited a variety of modes of regulation to exponentiate the range of
cellular behaviors possible with a limited set of protein-coding genes. A paradigm in
molecular biology has become that the genome does not just passively encode genes, but
rather that it carries a set of instructions to coordinate the expression of those genes in
time and space (Jacob and Monod, 1961). With stunning foresight, Jacob and Monod
postulated that proteins may recognize cis-regulatory DNA or RNA sequences and
thereby modulate the expression or translation of an mRNA (1961). Subsequent work has
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reinforced this model of transcriptional control by unraveling the genome-wide
architecture of protein binding events to cis-regulatory DNA elements (Ren et al., 2000;
Harbison et al., 2004). Similarly, it has been demonstrated that cis-regulatory sequences
within mRNA can orchestrate mRNA splicing, export from the nucleus to the cytoplasm,
localization, translation rate, and degradation rate (Glisovic et al., 2008).
Global measurements of transcription rate, mRNA degradation rate, translation
rate, and protein degradation rate among mRNAs confirms that each process is amenable
to regulation. The variability in the distributions of these rates cannot be accounted for as
a trivial result of measurement error, and in many scenarios the precise molecular
mechanisms explaining a proportion of the variability are known. Recent studies have
attempted to dissect the relative contributions of each form of regulation in explaining
steady state protein abundance. While initial estimates arrived at a conclusion that
variability in translational regulation was the predominant force determining protein
levels (Schwanhausser et al., 2011), revised estimates propose a predominant role of
transcriptional regulation, with about 73% contribution, relative to an 11% contribution
of mRNA decay, 8% contribution of translation rate, and 8% contribution of protein
decay (Li et al., 2014). However, these estimates ignore the fact that throughout
development, protein abundances are not at steady state, but rather change dynamically
with time in response to environmental and cellular signals. So far, it appears that
changes in mRNA levels (i.e., a combination of mRNA synthesis and degradation rates)
also explain ~90% of protein fold changes in a dynamic response to an environment cue,
although protein translation and degradation rates together explain ~60% of absolute
protein changes in this context (Jovanovic et al., 2015). Taken together, these studies
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