Systematic analysis of the role of differential expression of
microRNAs associated with cell death decisions
By
Nancy Guillen
B.S., Industrial Biotechnology
University of Puerto Rico, Mayagdez 2004
Submitted to the Biological Engineering Department in partial fulfillment of the
requirements for the degree of
AP
Doctor of Philosophy in Biological Engineering
MASSACHUSETT
I-NO"rE
OF TECHNOLOGY
at the
Massachusetts Institute of Technology
JUN 18 2014
May 2014
-BRARIES
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@ 2014 Massachusetts Institute of Technology. All rights reserved.
redacted
pSignature
Signature of author:
Certified by:
)
Department of Biological Engineering
Signature redacted
r /
Douglas A. Lauffenburger
Whitaker Professor of Biological Engineering, Chemical Engineering, and Biology
Thesis Supervisor
Signatu re redacted
Approved by:
Forest M. White
Professor of Biological Engineering
Graduate Program Committee Chairperson
LS
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THESIS COMMITTEE
Ernest Fraenkel
Associate Professor, Department of Biological Engineering
Massachusetts Institute of Technology
Thesis Committee Chair
Douglas A. Lauffenburger
Ford Professor, Departments of Biological Engineering, Biology, and Chemical Engineering
Massachusetts Institute of Technology
Thesis Supervisor
Phillip A. Sharp
Institute Professor, Department of Biology, Koch Institute for Integrative Cancer Research
Massachusetts Institute of Technology
Linda G. Griffith
Professor, Departments of Biological and Mechanical Engineering
Director, Center for Gynepathology Research
Massachusetts Institute of Technology
Forest M. White
Professor, Department of Biological Engineering
Massachusetts Institute of Technology
2
ABSTRACT
The link between abnormal microRNA expression and cancer has been widely
reported. However, little is known about the relationships between temporal microRNA
expression and changes in cell behavior. To better understand how microRNA expression
is involved in cell responses it is necessary to know what time dependent changes happen
in response to cellular stimuli. Here, we demonstrate that, in the hepatocellular carcinoma
(HCC) cell line Huh7, microRNA expression changes resulting from treatments with
different combinations of the cytokines IFN-y and TRAIL follow a time-dependent pattern
that correlates with cell death. An initial stimulus with IFN-y, followed by a second stimulus
with TRAIL is most effective at inducing cell death. By applying other combinations of these
two cytokines, we induce different levels of cell death after 48 and 72 hours of the initial
treatment. MicroRNA expression data from high throughput sequencing analysis was used
to construct data-driven multivariate models. Expression profiles associated to different
cytokine treatments were identified using principal component analysis (PCA) and, cell
death was defined as a function of microRNA expression using partial least square
regression (PLSR). Differential expression analysis was performed to identify relevant
microRNAs from the conditions most highly associated to cell death. Global microRNA
expression one hour after the second cytokine treatment is most predictive of cell death.
Several microRNAs were identified as strong predictors of cell death, including let-7c, miR181a and miR-92b, and others. Gene ontology analysis of the targets of these, and other
highly predictive microRNAs, suggests that there is an enrichment of apoptosis related
targets for the microRNAs that are up-regulated upon cytokine treatment. These studies
illustrate that the expression dynamics of microRNAs provide important insights into the
role of microRNAs in cell decisions processes, bringing us closer to designing new
strategies for diagnosis and treatment of HCC.
3
BIOGRAPHICAL NOTE
Nancy Guillen received a Bachelor of Science in Industrial Biotechnology degree, with
summa cum laude honors from the University of Puerto Rico, Mayaguez in June 2004. While
at the University of Puerto Rico, Nancy worked in the research laboratory of Prof. Belinda
Pastrana, where she used infrared spectroscopy to investigate conformational changes of
EGF and TGFa and how these conformations affect ligand-receptor interactions with EGFR.
Nancy also worked in the lab of Prof. Michael Spencer of Cornell University in the summer
of 2002, developing a collagen-based microfabricated molecular filter to separate biological
molecules. In the summer of 2003 she worked at the lab of Prof. Richard Roberts of the
California Institute of Technology, using NMR spectroscopy to study protein-RNA
interactions in the bacteriophage lambda-N protein. Lastly, in the fall of 2003, she worked
at Schering Plough Research in New Jersey, evaluating different parameters for therapeutic
protein purification using HPLC.
In September 2004, Nancy started his graduate work in the Department of Biological
Engineering at the Massachusetts Institute of Technology. Under the joint supervision of
David B. Schauer and Douglas Lauffenburger, Nancy worked on a project investigating the
effects on cellular signaling of intestinal cells interacting with pathogenic E. coli from 2005
to 2006. In 2007, Nancy took a leave from her graduate studies at MIT to work as a
consultant for Generans Bioventures in San Juan, PR. During her time as a consultant, she
evaluated biotech start-up companies for their investment value and was an instructor for
a class on the science behind the pharmaceutical and biotechnology industries at the UPR
Business School.
In 2008, under the supervision of Doug Lauffenburger, she started a Ph.D. thesis
project entitled "Systematic analysis of the role of differential expression of microRNAs
associated with cell death decisions", which was completed in May 2014. During her PhD,
Nancy was supported research fellowships from the NIGMS Biotechnology Training
Program and the MIT-Lemelson Presidential Fellowship. Her research was also supported
by the Cell Decision Process Center, the NCI Integrative Cancer Biology Program and US
Army Institute for Collaborative Biotechnologies.
While at MIT, Nancy was very active within the MIT community. She participated and
helped organized the Harvard-MIT Puerto Rican Caucus conference in 2007 and 2008 and
was involved in founding Mentes Puertorriquenas en Acci6n, which was started from the
caucus conference. She served as a program assistant for the MIT Summer Research
Program in 2008, 2009 and 2013. She was an organizer for the GSC Ski Trip in 2011 and
2012. She has been working part-time at the Muddy Charles Pub, the MIT community bar,
since 2008.
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ACKNOWLEDGEMENTS
First, I would like to thank my thesis advisors, Doug Lauffenburger, who is not only a
brilliant scientist and engineer, but also a supportive mentor and a great role model. His
vision was instrumental in shaping every aspect of this project. Doug allowed me to take
some risk and explore new areas of research in his lab. He learned about microRNAs with
me to take this project to a successful end. Doug maintains joint research laboratory with
Prof. Linda Griffith. Being part of the Lauffenburger and Griffith labs has been a great
privilege that allowed me to work with amazingly talented people on a daily basis. Being in
this environment help me gain an appreciation for rigorous quantitative cell and systems
biology. I have been extremely fortunate to have been advised by Doug, and to be a part of
the biological engineering community at MIT.
I would also like to thank a number of previous mentors that helped shape my
appreciation for research in biomedical fields. I am extremely grateful to my undergraduate
research advisor, Belinda Pastrana. She taught me how to conduct rigorous scientific
research, and how very exciting it can be when you have a passion for it. I am also grateful
to have had the opportunity to work with Prof. David Schauer, who met an untimely death
during my time at MIT. He was one of the most caring and supporting mentors and human
beings I've ever met and I am grateful to have been given the chance to work in his lab at
the beginning of my graduate career. I had the privilege to have another great mentor who
is not an academic, scientist or engineer, but an economist, investor and entrepreneur,
William Lockwood-Benet. I worked with William as a consultant when I took a leave of
absence from MIT and I learned how much of an impact the science we do has on our
economy and the lives of people that depend on it. He has also been an ally in a few other of
my endeavors.
I greatly appreciate the brilliant advice I've received from my thesis committee
members: Ernest Fraenkel, Phillip Sharp and Linda Griffith. They were all instrumental in
my research by providing amazing guidance and asking all the right questions about the
research I conducted here. They also allowed me to use resources from their own research
laboratories that were crucial in completing my final thesis work. I am also grateful to have
had Forest White join my committee for my thesis defense and for valuable conversations
we have had about my graduate work. They are all, not only great scientist that I am proud
to have met and worked with, but also great role models that I inspire me to continue doing
research that is interesting, relevant and well executed.
I will like to acknowledge my funding sources: NIGMS Biotechnology Training Program
and the MIT-Lemelson Presidential Fellowship, the Cell Decision Process Center, the NCI
Integrative Cancer Biology Program and the US Army Institute for Collaborative
Biotechnologies. These fellowship, programs, centers and grant not only provided the
funding for my research, but through meeting and conferences they also a supportive
scientific community to discuss my results and learn about advancements in related fields
of research.
This work would not have been possible without the efforts of a number of exceptional
co-workers and collaborators. I would like to thank the undergraduate students that have
worked with me in this project: Sarah Mok, Alfred Ramirez, Bryan Mejia-Sosa, Shanekkia
rII
0
Black, Justus Kebschull, and Brandon Crumsey. They were all hard working, exceptionally
smart students that helped me in different aspects of the research I conducted here. By
working with them, I learned how to be a mentor and how to teach other people the joys,
perils and tricks of being a scientist. They kept me striving to be better researcher and
biological engineer so I could guide them to learn all the techniques and the science behind
the work we did, give them freedom to ask their own questions and let them pursue
aspects of my research that were of interest to them. I will also like to thank Shelley Brown,
who has contributed greatly to the completion and editing of this thesis document. She
started working with me in the fall of last year and helped me with the quantitative PCR
work presented in chapter 3. Her friendship and support for these past few months have
been invaluable to me. I will also like to thank Margaret Ebert, a former graduate student in
the Sharp lab, who help me get started of this project and provided me with the microRNA
sponges described in Appendix A. Anthony Soltis, Adam Labadorf and Xiaofeng Xin in the
Fraenkel lab were very helpful in helping me complete the next generation sequencing
analysis. Stuart Levine, Vincent Butty, Kevin K. Thai, and the rest of the staff at the BioMicro
Center also had significant contributions to the next generation sequencing analysis.
Our laboratory manager Hsinhwa Lee, deserves special gratitude for the magnificent
efforts she contributes daily to the maintenance, organization and operational functionality
of our lab. Previous laboratory manager Stacey Pawson provided support for the earlier
parts of my project. Similarly, I would like to thank the following office and research staff
for their tremendous work: JoAnn Sorrento, Miroslava Parsons, Dan Darling, Aran Parillo,
Cathy Greene, and Michelle Berry. I will also like to thank my lab-mates: Rachel Pothier, TaChun Hang, Lorenna Buck, Brian Joughin, Shannon Alford, Kelly Chen, Jeremy Velazquez,
Samantha Dale, Jiajie Yu, Mohammad Ebrahimkhani, Sara Schrier, Jennifer Wilson, Lilly Xu
and everyone else that makes the Lauffenburger and Griffith labs an impressively great
place to do research at. I will like to give special thanks to Carissa Young for editing
chapters 1, 2 and 4 of this document.
I consider myself very fortunate to have made wonderful friends along the years I have
been here in Boston. In particular, I am grateful for the Muddy Charles bartenders,
especially Heather Keys, Edgar Sanchez, Andrew Davis, Eric Arndt, Sara Dubbury, Daisy
Riquelme, Izarys Rivera and our manager, Michael Grenier. Jaime Rivera, has been my
friend since before I came to MIT; he is my lab-mate, thesis writing buddy and almost like
family to me. I feel very fortunate to have him as a friend. Other amazing friends, lab-mates,
church friends, confidants, supporters, party friends, housemates, neighbors and
snowboarding buddies I will like to mention are (in no particular order): Retsina Meyer,
Juan Alvarez, Katie Villa, Dave Hall, Bill Hesse, Leah Schmidt, Francisco Sanchez, Yadira
Soto, Eliezer Calo, Sherlyn James, Jose Javier Diaz, Nelly Cruz, Jose Luis McFaline, Talya
Dayton, Roberto Giacalone, Amer Fejzic, Gloria Fleck (nee Satgunam). Alexandra Nesbeda,
Bethany Nine (nee Moatts), Maya Viswanathan, Joy Johnson, Indiara Deonadan, Grant
Eastman, Adam Reynolds, Mary Reynolds and a few other people that I am sure I am
forgetting to mention here. I will also like to thank my relatives in New York: Idelsa Guillen
and her family and Loida Jimenez and her family. They have been my cheerleaders and
provided me a home to go to midway from Boston to Puerto Rico.
(6
I could not have completed this work without the constant support of my family. Even
from the distance, they have been my biggest supporters and source of inspiration to
continue this work. My mom Damaris Marty and my sisters Dalisse Guillen and Lalisse
Guillen have given me all the love and support that fuelled my heart and soul to have the
energy and inspiration to complete the marathonic task of finishing a Ph.D. at MIT.
Lastly, I want to thank God, who I believe had something to do with me coming to MIT
and completing this work. I know he has plans greater than my own and everything I have
experience in my life so far is taking me where he wants me to be and where he needs this
newly minted doctor to work for his kingdom.
(7
TABLE OF CONTENTS
THESIS COMMITTEE..........................................................................................................................................2
A B ST RA C T ..............................................................................................................................................................
3
BIOGRAPHICAL NOTE .......................................................................................................................................
4
ACKNOW LEDGEMENTS....................................................................................................................................5
LIST OF FIGURES AND TABLES .................................................................................................................
12
1.
14
2.
In tro d u ctio n ..............................................................................................................................................
1.1
Hepatocellular carcinoma .........................................................................................................
14
1.2
Cellular signaling in cell death and survival decisions ..................................................
17
1.3
MicroRNAs in translational regulation and cancer ........................................................
20
1.4
Computational analysis of cellular signaling and gene expression data................. 23
1.5
T h esis Ov erview ................................................................................................................................
Cytokine induced cell death in hepatocytes........................................................................
2 .1
In tro du ctio n ........................................................................................................................................
26
28
28
2.1.1
Cytokine induced apoptosis and HCC cell line models .............................................
28
2.1.2
Ch apter overview .........................................................................................................................
29
Experimental Procedures.........................................................................................................
30
2.2
2.2.1
Apoptosis response to death receptor ligands by measuring caspase 3 and PARP
u sing flow cytom etry ....................................................................................................................................
30
2.2.2
Cell viability in response to IFNy and TRAIL treatment ..........................................
2.2.3
Hepatocyte cytotoxicity measured by activity of LDH released in the media...... 32
2 .3
R e su lts...................................................................................................................................................
31
34
2.3.1
Apoptosis response to death receptor ligands by measuring caspase 3 and PARP
u sing flow cytom etry ....................................................................................................................................
34
2.3.2
Cell viability in response to IFNy and TRAIL treatment ..........................................
2.3.3
Hepatocyte cytotoxicity measured by activity of LDH released in the media...... 40
2 .4
Discu ssio n ............................................................................................................................................
36
41
3. Measurements of microRNA expression changes induced by IFNy and TRAIL in
H C C ce ll lin e s......................................................................................................................................................
43
3 .1
3.1.1
In tro d u ctio n ........................................................................................................................................
MicroRNA expression in TRAIL and interferon signaling ........................................
(8
43
43
3.1.2
Platforms for expression profiling of microRNAs .....................................................
44
3.1.3
Ch ap ter overview .........................................................................................................................
45
Experimental Procedures.........................................................................................................
46
3.2
3.2.1
Global microRNA expression profiling using bead based assays on TRAIL
stim ulated H C C cells .....................................................................................................................................
46
3.2.2
Global microRNA expression profiling using microarray technology on TRAIL
stim ulated H C C cells .....................................................................................................................................
47
3.2.3
Analysis for bead based assay and microarray expression data ..........................
47
3.2.4
Global microRNA expression profiling using next generation sequencing on
Huh7 cells systematically treated with IFNy and TRAIL...........................................................
48
3.2.4.1 Data analysis for Illumina sequencing ...........................................................................
48
3.2.5
50
3 .3
Quantitative PCR using LNA probes ................................................................................
R e sults...................................................................................................................................................
50
3.3.1
Global microRNA expression profiling using bead based assays on TRAIL
stim u lated H C C cells .....................................................................................................................................
50
3.3.2
Global microRNA expression profiling using microarray technology on TRAIL
stim u lated H C C cells .....................................................................................................................................
54
3.3.3
Global microRNA expression profiling using next generation sequencing on
Huh7 cells systematically treated with IFNy and TRAIL...........................................................
56
3.3.3.1
Abundance of microRNAs ...................................................................................................
58
3.3.3.2
Differentially expressed microRNAs ................................................................................
60
3.3.4
Q uan titative PC R ...........................................................................................................................
62
3 .4
4.
D iscu ssio n ............................................................................................................................................
63
Computational analysis of microRNA expression and cell death ............................
65
4 .1
In tro d u ctio n ........................................................................................................................................
65
4.2
Experimental Procedures.........................................................................................................
65
4.2.1
Multivariate analysis of microRNA expression data using principal component
a n a lysis (P C A ) .................................................................................................................................................
65
4.2.2
Multivariate analysis of microRNA expression data using partial least square
reg re ssio n (P L S R)..........................................................................................................................................
66
4.2.3
Model reduction by treatment time and microRNA relevance criteria ..............
4.2.3.1
Model reduction by time points and microRNA correlation to cell death........ 67
(9
67
4.2.3.2
Model reduction by variable importance of projection (VIP) scores..................
4.2.3.3
Model reduction by microRNA abundance and high confidence status............ 68
68
4.2.3.4 Model reduction by time points and differential expression analysis ...............
68
4.2.4
Gene ontology analysis...............................................................................................................
69
R e sults...................................................................................................................................................7
0
4 .3
4.3.1
Multivariate analysis, using principal component analysis of microRNA
70
expression categorizes the conditions according to cell response ........................................
4.3.2
Multivariate analysis using partial least square regression defines a function to
predict cell death from microRNA expression ..............................................................................
71
73
4.3.3
Model reduction by treatment time and microRNA relevance criteria ..............
4.3.3.1
Model reduction by time points and microRNA correlation to cell death......... 73
4.3.3.2
Model reduction by variable importance of projection (VIP) scores..................
75
4.3.3.3 Model reduction by microRNA abundance and high confidence status............ 77
4.3.3.4 Model reduction by differentially expressed microRNAs .......................................
78
4.3.4
80
4.4 .
5.
G en e on tology ................................................................................................................................
Discu ssio n ............................................................................................................................................
Conclusions and future direction.............................................................................................
81
83
5.1
Emergence of systems biology analyses for microRNA expression in the context of
83
cell decisions and hepatocellular carcinoma .................................................................................
5.2
Cytokine induced cell death in hepatocytes.....................................................................
84
5.3
Measurements of microRNAs expression changes induced by IFNy and TRAIL in
H C C ce ll lin e s ...................................................................................................................................................
84
5.4
Computational Analysis of microRNA expression and cell death.............................
85
5.5
Futu re d irection s ..............................................................................................................................
87
APPENDICES....................................................................................
88
Appendix A: Perturbation of microRNA activity by mimics and sponges...........................
88
A .1 In tro du ctio n .............................................................................................................................................
88
A .2 Experim ental procedures ...................................................................................................................
88
A .2 R e su lts ........................................................................................................................................................
89
A .2 D iscu ssio n .................................................................................................................................................
90
Appendix B: Tables for evaluation of microRNAs for model reduction based on relative
abundance and high-confidence..................................................................................................................
(10
)
91
B.1
Relative microRNA expression data from a systematic approach to cytokine
treatment and cell death experiment measured by next generation sequencing on the
Illu m in a p latfo rm ...........................................................................................................................................
91
B.2
High confidence microRNAs for reduced PLSR model ..................................................
R EF E R E NC ES.......................................................................................................................................................
(11
95
96
LIST OF FIGURES AND TABLES
Figure 1. Progression from liver damage to hepatocellular carcinoma...................................
16
Figure 2. Cellular signaling pathways involved in cell death and survival decisions in HCC.
...................................................................................................................................................................................
20
Figure 3. Experimental design for a systematic approach to measure cell death and
m icroR N A expression......................................................................................................................................
34
Figure 4. Effect of TNF family ligands on apoptotic responses in primary human
hepatocytes and H CC cell lines.....................................................................................................................
35
Figure 5. Cell viability determines cytokine concentration dosage and cell density
co n d itio n s..............................................................................................................................................................
37
Figure 6. Cell viability for Huh7 cells treated with 10ng/mL IFNy and different
concentrations of T RA IL..................................................................................................................................
39
Figure 7: Different IFNy and TRAIL treatment in Huh7 cells cause different levels of
cytotoxicity at 48 and 72 hours....................................................................................................................
41
Figure 8: Comparison of different platforms for measuring microRNA expression in terms
of multiplexing and high-throughput ranges......................................
45
Figure 9. Global microRNA expression of Huh7 and Hep3B measured with the Luminex
bead b ased assay ................................................................................................................................................
51
Figure 10: Hierarchical clustering of global microRNA expression of Huh7 and Hep3B
measured with the Luminex bead based assay.................................................................................
52
Figure 11: Analysis of microRNA expression data from Exiqon microarrays.......................
54
Figure 12: Hierarchical clustering of microRNA expression data from Illumina sequencing.
..................................................................................................................................................................................
57
Figure 13: Expression patterns of ten most abundant microRNAs across all conditions,
detected by next generation sequencing .............................................................................................
59
Figure 14. Expression of microRNAs differentially expressed at after 25 hours of IFNy
treatment and 1 hour of TRAIL treatment..........................................................................................
61
Figure 15. Quantitative PCR on microRNAs differentially expressed at 25 hours after initial
tre atm e nt...............................................................................................................................................................
(12
J
62
Figure 16. PCA analysis of microRNA expression for different cytokine treatments......... 71
Figure 17. PLSR model loadings plot in principal component space........................................
72
Figure 18: Evaluation of PLSR models with different subsets of expression data...............74
Figure 19. Cumulative distribution of VIP scores for microRNAs expressed at all time points.
...................................................................................................................................................................................
75
Figure 20. Top 20 microRNAs ranked by VIP scores.....................................................................
76
Figure 21. Model including high confidence microRNA list ........................................................
77
Figure 22. Model performance by reduction to top 10 abundant microRNAs......................
78
Figure 23. Model reduction by differentially expressed microRNAs........................................
79
Figure 24. Survival and apoptosis pathways in HCC and possible targets of relevant
m icro R NA s8............................................................................................................................................................
Table 1: Cytokine doses for measurements of cell viability response
86
32
Table 2: Cytokine treatments for cytotoxicity and microRNA measurements ......................
33
Table 3: List of treatments for microRNA expression measurements using a systematic
a p pro a ch ................................................................................................................................................................
49
Table 4.A-C: Differentially expressed microRNAs, measured by Luminex bead based assay
...................................................................................................................................................................................
53
Table 5: Differentially expressed microRNAs, measured by Exiqon microarrays to look at
expression changes induced by TRAIL .................................................................................................
55
Table 6: Ten most abundant microRNAs detected by next generation sequencing...........59
Table 7: Differentially expressed microRNAs at 25 and 36 hours.............................................
60
Table 8. M icroRNAs highly correlated to cell death ........................................................................
73
Table 9: Model evaluation for several PLSR models reduced by time course.......................
74
Table 10. Model reduction by VIP scores and time points and the effect on model
p erform an ce p aram eters................................................................................................................................
76
Table 11. Gene ontology terms enriched in targets of 12 differentially expressed
m icroRNAs at 25 hours post-initial treatm ent...................................................................................
(
13
81
CHAPTER 1
1.
Introduction
This thesis investigates the relationship between temporal microRNA expression
and cellular death, in cell line models of hepatocellular carcinoma (HCC), by integrating
quantitative, experimental and computational cell biology approaches. Chapter 1 contains
background and motivating information pertaining to hepatocellular carcinoma, cell death
and associated signaling pathways, microRNA biology and computational systems biology
methods fundamental to this thesis.
1.1
Hepatocellular carcinoma
The liver is the largest internal organ. It is responsible for several vital functions
including the metabolism and storage of nutrients absorbed from the intestines, secretion
of bile into the intestines, blood detoxification and removal of microbes. As such, the liver is
composed of multiple cell types highly organized in a three-dimensional structure.
Hepatocytes, or parenchymal cells, are the key cell type for liver functions. Nonparenchymal liver cells (NPC's) include bile duct epithelial cells, sinusoidal endothelial cells
(SEC's), Kupffer cells, and stellate cells [1-3].
The liver has a remarkable capacity for regulating its growth and mass. After injury,
liver cells regenerate to maintain liver function, structure, and size. For example,
hepatocytes and other liver cells are capable of rapid proliferation after partial
hepatectomy. Hepatocytes must maintain a balance between proliferation, senescence and
cell death to enable liver regeneration without overindulging in cell proliferation. A cohort
of growth factors and cytokines regulate hepatocyte differentiation and proliferation. A
perturbation in the balance of these pro-survival and pro-apoptotic factors leads to liver
diseases such as fibrosis and cancer [4, 5]. These pro-survival and pro-death signaling
pathways are involved in carcinogenesis and pathogenesis of primary liver cancer, also
known as hepatocellular carcinoma (HCC).
(14
)
Hepatocellular carcinoma originates from damaged hepatocytes
and is the
predominant form of primary liver cancer, accounting for 80% of the cases. Another type of
primary liver cancer, cholangiocarcinoma, originates in the intrahepatic bile ducts. It is also
common for metastases of tumors in distant sites to develop in the liver. The most recent
report by the American Cancer Society indicates that the rates of primary liver cancers
have been steadily rising among the population, with 33,190 estimated new cases and
23,000 estimated deaths for 2014. In the US and other Western countries, the majority of
cases are due to alcohol-related cirrhosis, nonalcoholic fatty liver disease, and related
metabolic disorders. Chronic hepatitis B virus (HBV) and hepatitis C virus (HCV) infections
are the major risk factors for the disease worldwide. Other factors that increase the risk of
liver cancer include certain genetic disorders, such as hemochromatosis, some parasitic
infections more prevalent in economically developing countries (e.g. schistosomiasis and
liver flukes) and consumption of food contaminated with aflatoxin, a toxin produced by
mold during the storage of agricultural products in a warm, humid environment. Figure 1
depicts the events leading to the development of HCC. Rates of HCC are higher in areas
where HBV is endemic and food contamination with aflatoxin is common, which includes
China, Southeast Asia, and sub-Saharan Africa. HCC is more prevalent among men ages 45
and older [6, 7].
Early stage liver cancer can sometimes be successfully treated with surgery in a
limited number of patients with sufficient healthy liver tissue. Liver transplantation may be
an option for individuals with small tumors that cannot be surgically removed. Alternative
treatment options include ablation (tumor destruction) or embolization (blocking blood
flow to the tumor). Fewer treatment options exist for patients diagnosed at an advanced
stage. Sorafenib (Nexavar) is a targeted drug approved for the treatment of HCC in patients
who are not candidates for surgery. The overall 5-year relative survival rate for patients
with liver cancer is 16%. Forty-one percent of patients are diagnosed at an early stage, for
which 5-year survival is 29%, up from 9% in the mid-1970s. Survival decreases to 10% and
3% for patients who are diagnosed at regional and distant stages of disease, respectively
[6]. Unfortunately, most therapeutic approaches for HCC fail to deliver significant clinical
(
15
improvement, in terms of patient morbidity and survival. Therefore, there is great potential
for improving prognosis by investigating potential new treatments and diagnostic tools.
Liver damage:
Infectious: HCV, HBV
Tbxic:Aflatoxin, alcohol
Chronic hepatitis and rapid
hepatocyte regeneration ,
Pre-neoplastic
Cirrhosis
(scarring)
stage
Genetic
alterations
Hepatocellular
carcinoma
Figure 1. Progression from liver damage to hepatocellular carcinoma.
Several mouse and cell line models are used to study HCC in pre-clinical settings [815]. These biological models of disease progression are useful in evaluating different
aspects of the disease and investigating the efficacy of different treatment approaches.
Epithelial to mesenchymal transition (EMT) is a common event in carcinogenesis. A
number of HCC cell lines have been characterized as being more epithelial-like or more
mesenchymal-like in terms of their phenotypic responses and gene expression patterns.
Fuchs et al. [9] evaluated the expression of E-cadherin and vimentin to determine the
extent of EMT among 12 human HCC cell lines and evaluate the correlation of EMT to cell
responsiveness to the EGFR inhibitors erlotinib and cetuximab. Five cell lines (i.e., SNU-423,
HepG2, Hep3B, HuH-7, and PLC/PRF/5) were classified as epithelial, based on E-cadherin
expression assayed by Western blot analysis. Seven cell lines (i.e. SNU-182, SNU-387, SNU475, Focus, SK-Hep, SNU-449, and SNU-398) were considered to be mesenchymal because
they lack E-cadherin, although these cell lines express vimentin at varying levels.
16
Microarray analysis of human hepatoma cell lines by Lee and Thorgeirsson [8] reveals two
distinctive subtypes. Group I is characterized by the activation of oncofetal promoters
leading to increased expression of AFP and IGF-II. Group II is characterized by
overexpression of genes involved in metastasis and invasion, such as CD44 and ILK. These
studies demonstrated that EMT strongly correlates with HCC subtypes that are based on
invasive gene expression profiles.
1.2
Cellular signaling in cell death and survival decisions
A few of the main cellular signaling pathways are involved in cell death and survival
decisions in HCC cells (Figure 2). The Ras/MAPK pathway is a ubiquitous intracellular
mechanism that eukaryotic cells use to process information from the environment. It is
involved in the regulation of stress responses, proliferation, inflammation, metabolism and
many other cell functions. In mammalian cells, there are three major MAPK pathways:
extracellular signal-regulated protein kinases (ERKs), the c-Jun N-terminal kinases (JNKs)
and the p38 family of kinases [16]. Activation of the MAPK pathway occurs by various
mechanisms, including the modification of transmembrane receptors EGFR or ErbB
receptor tyrosine kinase (RTK) family, and in the case of hepatocytes the c-Met receptor.
Growth factors, such as epidermal growth factor (EGF), transforming growth factor alpha
(TGF-a) and hepatocyte growth factor (HGF), and others bind RTK's, eliciting a change in
conformation and autophosphorylation in their intracellular domain, which activates a
signaling cascade. These receptors also activate the PI3K/Akt pro-survival pathway.
Sorafenib, for example is a multi-kinase inhibitor that targets Raf/MEK/ERK signaling at
the level of Raf kinase, and the receptors tyrosine kinase VEGFR-2/-3 and PDGFR-P, which
are involved in angiogenesis. Notably, Sorafenib has proven to be effective in increasing
survival time for patients in phase II and III clinical trials [17]. Another pro-survival
pathway which is dysregulated in HCC is the Wnt/P-catenin pathway. Activation of Wnt
receptors results in phosphorylation of P-catenin by GSK3p. Active P-catenin translocate to
the nucleus where it acts as a co-activator of anti-apoptotic factors [4].
f
17
The interferon gamma (IFNy) pathway is also affected in HCC. Ethanol-induced
oxidative stress causes a reduction in tyrosine phosphorylation of signal transducer and
activator of transcription 1 (STAT1). STAT1-directed activation of IFNy signaling decreases
and the protective effects of IFNy are lost, resulting in hepatocyte damage. Oxidative stress
might also cause the accumulation of oncogenic mutations. For example, increased
oxidative stress associated with iron overload (hereditary hemochromatosis) has been
associated with p53 mutations in resultant HCCs.
Apoptosis, or programmed cell death, is a highly regulated process involved in
tissue development and disease. There are two signaling pathways leading to apoptosis:
the death receptor or extrinsic pathway, and the mitochondrial or intrinsic pathway. In
both cases, the main mediators of apoptosis are cysteine proteases, referred to as caspases
[18]. These proteins are produced as zymogens, or catalytically inactive precursors that
need to be proteolytically cleaved during apoptosis to be activated. Autocleavage of the
initiator caspases (caspase-8 and -9) initiate a caspase cascade that facilitates cleavage of
other effector caspases (caspase-3,-6 and -7) and the release of cytochrome C by the
mitochondria. The activated effector caspases then cleave cytoskeletal components and
regulators (e.g. cytokeratin 18, PARP), leading to disruption of the nuclear envelope, and
DNA fragmentation. Characteristic features of apoptotic cells include the redistribution of
phosphatidylserine in the cell membrane, cell membrane blebbing and breaking into
vesicles called apoptotic bodies.
In the liver, inflammatory and pro-apoptotic cytokines are primarily released by the
resident liver macrophages, or Kupffer cells, following injury. These cytokines include
interferon-y (IFN-y), tumor necrosis factor-a (TNFa), Fas ligand (FasL), TNF related
apoptosis inducing ligand (TRAIL), transforming growth factor-P (TGF-P) and others [19,
20]. Incidentally, neither one of these cytokines is sufficient to induce cell death alone, but
rather, a combination of these cytokines effectively cause hepatocytes to undergo apoptosis.
TNFa, FasL and TRAIL bind to death receptors in the cell membrane, which in turn activate
the extrinsic apoptosis pathway. The Fas and TRAIL mediated apoptosis-signaling pathway
is shorter than that of TNFa. Fas and TRAIL-R activation take only hours to kill target cells,
while TNFa takes a day or more. TNFct can activate the mitochondrial pathway indirectly
(
18
)
[21]. In the liver, TGF- is normally produced by stellate cells and its role is to regulate the
removal of damaged cells and to maintain normal cellular homeostasis and organ size. It
limits the growth of hepatocytes in response to injury by inhibiting DNA synthesis, blocking
cell cycle progression and inducing apoptosis [4].
The effect of TRAIL in some tumors is particularly interesting because it can cause
apoptosis in tumor cells but not in the adjacent non-cancerous tissue. Collectively, these
results suggest that in normal cells there exists a balance between two types of receptors
that bind TRAIL in the cell surface: decoy receptors DcR1 and DcR2, and death receptors
DR4 and DR5. Some tumor types over-express DR4 and DR5 [22, 23]. Consequently, this
overexpression is associated with oncogenic c-Myc expression. For a number of years,
there has been a lot of interest in advancing TRAIL to clinical trials in different recombinant
formulations as a possible therapeutic for liver cancer [24, 25]. The effect of TRAIL in
cancer cells varies significantly in cancers originating from different tissues. In some types
of cancers, including HCC, TRAIL does not elicit an apoptotic response by itself. This nonresponsiveness to TRAIL can be overcome by sensitizing the cells with certain
chemotherapeutic agents or cytokines, [26-29]. It has been shown that miR-221 and miR222 play a role in TRAIL resistance in certain types of cancer [30, 31]. This allows for a very
interesting system to study because further investigation will increase our understanding
of how these cells become sensitized to TRAIL for apoptosis. Interestingly, the proinflammatory cytokine, IFNy can sensitize cells to the apoptotic effects of TRAIL by up
regulating the interferon regulatory factor (IRF)-1 [27]. IFNy also regulates the expression
of several microRNAs through STAT signaling [32, 33]. The deadly combination of these
two cytokines, TRAIL and IFNy, and their regulation of miRNA expression and cell death in
hepatocytes will be the focus of this thesis.
r19
TNFa
TRAIL
sL
RTKjF
y
Writ
/-6
receptor
transcription
miRNAs
Figure 2. Cellular signaling pathways involved in cell death and survival decisions in HCC.
1.3
MicroRNAs in translational regulation and cancer
MicroRNAs (miRNAs) are small endogenous oligonucleotides that regulate protein
expression. Since they were discovered in C. elegans [34, 35] hundreds of microRNAs have
been identified in the genomes of many metazoan organisms. MicroRNAs function in a
number of cellular processes, including differentiation, metabolism, proliferation and cell
death [36]. To regulate protein translation, microRNAs use the RNA interference pathway,
which they share with small interference RNAs (siRNAs). Unlike siRNAs, which are
exogenously introduced to the cell, microRNA expression is controlled by transcription.
Their genes may be located within introns of host genes or clustered in polycistronic
transcripts [37]. Mature microRNAs are derived from larger precursors to form imperfect
stem-loop structures. Maturation occurs by sequential cleavage catalyzed by two RNase-III
enzymes: Drosha in the nucleus and Dicer in the cytoplasm. Dicer produces a small
imperfect double stranded duplex with the mature microRNA and its complementary
strand. Dicer also cleaves exogenous dsRNA into siRNA duplexes. For both microRNA and
20
siRNA, one strand of the duplex is preferentially incorporated into the RNA induced
silencing complex (RISC) in the cytoplasm to form the working translational repression
machinery. The role of microRNAs in RISC is to act as the probes to find target mRNAs.
Within RISC, microRNAs bind with imperfect complementarity to their target, while siRNAs
bind with perfect complementarity. Argonaute (Ago) proteins are the components of RISC
that are responsible for the repression of translation from mRNA to proteins. The current
understanding of gene regulation by microRNA is that they act by destabilizing their target
mRNAs[38]. Target sequences for microRNAs are typically located in the 3' UTR region of
mRNAs, thus allowing for translational repression of specific genes. Genes can have
multiple binding sites for different microRNAs. Additionally, each microRNA can
potentially target a large number of genes. Therefore, changes in expression of microRNAs
orchestrate changes in protein expression that can affect cellular pathways systematically,
rather than targeting individual proteins.
It is estimated that more than 60% of the human genome is regulated by these small
non-coding RNAs [39, 40]. The latest version of miRBase, the microRNA database, includes
1872 known human microRNA sequences. However, many of these sequences come from
small RNA deep sequencing experiments which may have misidentified new microRNA
sequences which could be fragments of transcripts or other types of small RNAs. Out of the
annotated sequenes, 278 human microRNAs meet a set of criteria and classified as highconfidence microRNAs [41]. Targets of microRNAs can be predicted computationally and
validated experimentally. Currently, TargetScan is the go-to database to find information
for predicted microRNA targets [42, 43].
Expression patterns of microRNAs have proven to be very valuable for diagnostic,
prognostic, and therapeutic uses [44-47]. Several well-established platforms for evaluating
gene expression, such as microarray, cloning, northern blotting, quantitative real-time-PCR
(qRT-PCR), in situ hybridization (ISH), and next generation sequencing (NGS) are now
being used to measure microRNA expression [36, 41, 48-51].
However, some unique
features of microRNAs, such as their small total number and short length, have created
technical challenges for direct application of various array platforms. It is necessary to
modify these technologies to measure microRNA expression with high specificity and
21]
sensitivity. Several efforts to compare and normalize different profiling technologies have
been implemented to get more accurate and precise microRNA expression results. These
efforts have had various degrees of success.
Aberrant microRNA expression has become a hallmark of cancer. Different types of
malignancies present different microRNA expression profiles that are significantly altered
from those of their normal tissue counterparts; HCC is no exception [52-55]. Several
studies have explored the link between changes in expression of microRNAs and
carcinogenesis in HCC. For example, mir-21 is up-regulated and the liver specific mir-122
is down regulated in most HCC cases. A putative target of miR-21 is PTEN, which is an
inhibitor of P13K, a pro-survival protein kinase [56]. Cyclin G1 is a putative target for miR122 [57] and a negative regulator of p-53. These studies focus on finding differential
microRNA expression of liver samples from normal and tumor tissues with different
phenotypes and origins. However, the analysis of temporal changes in microRNA
expression that occur in response to cell stimulus, and the correlations of these profiles to
changes in cell phenotypes are aspects that remain largely unexplored.
It has been suggested that specific microRNAs can be targeted as a cancer therapy.
It might be possible, then, that a treatment which restores the balance at the microRNA
level can be used as a therapy for liver cancer. Various approaches to target the RNA
interference pathway to regulate protein expression levels exist. There are different classes
of microRNA inhibitors including: chemically modified anti-sense oligonucleotides [58-60],
rigid RNAs produced from modified RNA nucleotides called locked nucleic acids (LNA) [61],
and RNAs produced from transgenes called sponges [62]. SiRNA technology and microRNA
mimics can be used to substitute for the effect of endogenous microRNAs that are downregulated. There have been several attempts at exploring microRNAs as target of
therapeutic intervention for the treatment of HCC. One particularly promising example is
miR-26a [12]. Using adeno-associated virus (AAV) as a vector, miR-26a was administered
in a mouse model of HCC resulting in inhibition of cancer cell proliferation, induction of
tumor-specific apoptosis, and protection from disease progression.
Few studies have evaluated the temporal regulation of microRNA expression, and
there is much that can be learned from the time frames in which changes in microRNA
f
22
j
expression occur, and how the expression dynamics help them fulfill their function in cells.
Much of the work that has been done with regards to temporal microRNA expression
comes from animal cell development, particularly in C. elegans, where dynamic regulation
of microRNAs is crucial in determining cell fate. A few studies have shown that
transcriptional regulation of microRNAs respond to environmental cues. For example,
expression of the liver specific miR-122 show changes within 30 min after treatment with
IFN3 in Huh7 cells [63]. In melanoma cells 8.9% of 1105 microRNAs that were measured
appeared to be directly or indirectly regulated by stimulation with interferon gamma
(IFNy), which activates the transcription factor STAT1. The majority of robust microRNA
expression changes in this study occurred in an intermediate time range (24 to 48 hours)
[33]. In human glioma cells, IFN-P treatment suppressed the growth of glioma-initiating
cell-derived intracranial tumors and markedly reduced miR-21 expression 6 hours after
treatment, indicating that the reduction in miR-21 levels was due to transcriptional
suppression [32]. The addition of signal transducers and activators of transcription 3
(STAT3) - expressing vectors induced the IFN- P - mediated suppression of miR-21,
whereas STAT3-inhibiting agents inhibited the miR-21 suppression, thus miR-21 temporal
down regulation is negatively regulated by STAT3 activation. In another study, Yang, et al.
demonstrated that IFNa-induced up regulation of miR-21 is an early event (after 2 hours of
IFNa treatment) in a number of human cell lines from different tissues [64].
1.4
Computational analysis of cellular signaling and gene expression data
Over the past couple of decades, computational biology has established itself as a
thriving field of research within both biology and computational sciences. Gene expression
from microarrays and next generation sequencing, as well as other "omics" related
technology presented an array of complex problems to which previously developed
algorithms and modeling techniques have been applied. A few years ago, computational
biology was mainly focused on statistics based data mining approaches and bioinformatics,
with the goal of sorting through millions of gene expression values to find lists of genes
relevant for specific phenotypic behaviors. Advances in multiplexing, high throughput
assays, and quantitative technologies for biological research resulted in a multitude of
(
23
studies involving massive amounts of related data from different parts of the cell and
different sources. Rather than taking a reductionist approach focusing on individual
biological parts and their specific functions, systems biology is a holistic approach to
understand biological processes and how different parts of a system work together.
Systems biology is based on the idea that biological systems are complex, involving
different sets of elements that interact selectively, often in a non-linear fashion, and are
regulated at different levels [65]. Combining perturbation experiments with highthroughput and multi-plexed assays allowed us to find connectivity and functionality in
biological systems by analyzing how different parts of a whole define complex processes.
Systems biology paradigms have been applied mostly in the realm of molecular and cellular
biology, with the goal of attaining a better understanding of the details of cellular of
signaling networks at the mechanistic and dynamic level and be able to predict certain
behaviors from a series of inputs and signals [66]. However, the same approaches can as
well be used for higher level systems, such as tissues, organs and even populations and
communities of different organisms.
Several computational approaches are used to evaluate biological systems,
depending on the level of understanding of the systems of interest, the nature of the data
and the hypothesis that is being tested [67-69].
Physicochemical modelling use prior
knowledge to describe biomolecular transformations in terms of equations derived from
established physical and chemical theory. Data driven models allow multivariate biological
measurements to reveal new surprising and unanticipated biological insights even when
prior knowledge of a particular pathway is minimal. Multivariate analysis has proven to be
a powerful tool in analyzing highly dimensional and extensive biological data, such as
kinase activity of multiple proteins of a network, and gene expression data. It is also
possible to use protein signaling and gene expression data in combination with
measurements of phenotypic behavior to determine correlations and co-variation between
the different stages of cell responses to stimuli.
A data-driven approach is often the choice for data sets including diverse cellular
elements. Previously, cue-signal-response approaches have been used successfully to
follow the information flow in a system and to develop predictive mathematical models of
(
24
)
cell survival and apoptosis in human epithelial cells [70-72]. To understand the
connections within a heterogeneous array of information, it is necessary to first validate
and normalize all the data from bench work experiments before including it as part of a
mathematical model. Once this is accomplished, multivariate regression and analysis
methods such as partial least square regression (PLSR) and principal component analysis
(PCA) can be used to organize highly dimensional collections of signaling measurements
and to reduce the dimensionality of the data set to select for the most predictive markers
for specified outcomes. Measuring a set of signals at the protein and RNA level allows for
the development of a comprehensive mathematical model. Using PLSR and PCA to analyze
microRNA expression data may provide a better understanding the role of microRNA in
cellular phenotypic behavior.
PCA is a method to analyze and visualize highly dimensional data. The PCA method
generates a new set of variables, called principal components, which are linear
combination of the original variables. All the principal components are orthogonal to each
other, so there is no redundant information. The principal components as a whole form an
orthogonal basis for the space of the data. The first principal component is a single axis in
space. When you project each observation on that axis, the resulting values form a new
variable, and the variance of this variable is the maximum among all possible choices of the
first axis. The second principal component is another axis in space, perpendicular to the
first. Projecting the observations on this axis generates a second new variable. The variance
of this variable is the maximum among all possible choices of this second axis. The full set
of principal components is as large as the original set of variables, but it is common to find
that the sum of the variances of the first 2 or 3 principal components exceeds 80% of the
total variance of the original data. PCA is completed by eigenvalue decomposition of a data
covariance (or correlation) matrix or singular value decomposition of a data matrix, usually
after mean centering (and normalizing or using Z-scores) the data matrix for each attribute
[73]. PCA results are usually discussed in terms of component scores and loadings. Scores
correspond to the transformed variable values of a particular data point, and loadings are
the weight by which each standardized original variable should be multiplied to get the
component score.
(
25
Partial least-squares regression (PLSR) is something of a cross between multiple
linear regression and principal component analysis. The technique is used with data that
contain correlated predictor variables. Similarly to PCA, this technique constructs new
predictor variables, known as components, as linear combinations of the original predictor
variables. PLSR constructs these components while considering the observed response
values, leading to a linear model with reliable predictive power.
Gene expression data is commonly analyzed using multivariate analysis methods
such as PCA and PLSR. These studies include quantitative measurements of thousands of
genes, requiring the use of computational techniques to analyze the data and classify genes.
There are several other techniques used to make sense of this data, going from clustering of
genes with similar expression patterns across many different samples, to finding
differential expression from pairwise comparisons of differing biological conditions [74]. A
noteworthy technique for finding differentially expressed genes is DESeq, a package
developed for the software R, which assumes a negative binomial distribution of the data to
complete pairwise comparisons between sets of data from counts of reads, common in
next generation sequencing [75].
With these computational tools, along with a systems mindset, and biological
knowledge and intuition, we set out to find meaning from time-resolved microRNA
expression data that come from cells treated with a pro-apoptotic combination of cytokines
in the context of primary liver cancer. Our hope is that this study will provide a better
understanding of the dynamic regulation of microRNA, its role in cell decision processes
and provide possible diagnostics markers and therapeutic targets for HCC.
1.5
Thesis Overview
Here, we explore possible correlations between IFN-y and TRAIL stimulus (cue)
with temporal microRNA expression (signals) and cell death (response) in a HCC model
with the purpose of investigating correlations between external perturbation, the resulting
microRNA expression changes along with cell phenotypic responses and the time frames at
which all of these processes are orchestrated. Huh7 human cell line was used as a model for
f
26
J
HCC. Cell death was measured with flow cytometry, cell viability by dye exclusion and
cytotoxicity by LDH activity. Global microRNA expression was measured using the Luminex
bead based assay, microarray technology, quantitative PCR and high throughput
sequencing. We used PCA and PLSR to analyze microRNA expression data in more depth
and identify individual microRNA and expression patterns that correlate with cell death.
Similarly, DESeq was used to identify differentially expressed microRNAs across relevant
time-points and conditions. These studies may provide a better understanding of temporal
regulation of microRNAs, their role in cell phenotypic behavior and may provide an avenue
for new therapeutic approaches for HCC.
27
CHAPTER 2
2.
Cytokine induced cell death in hepatocytes
2.1
Introduction
2.1.1
Cytokine induced apoptosis and HCC cell line models
Cytokines are crucial players in the well-orchestrated process of apoptosis.
Members of the tumor necrosis factor (TNF) family of ligands, in particular, can directly
induce apoptosis in a number of tissues. Several TNF family ligands and receptors are
expressed in liver tissue. These pro-apoptotic cytokines act in sync with growth factors and
other cytokines to maintain tissue homeostasis by keeping a balance between pro-survival
and pro-apoptotic signals. This balance can be lost in a diseased liver; when the balance is
tipped in favor of cell proliferation, hepatocarcinogenesis may occur [4].
TRAIL is a promising candidate for cancer therapy due to its ability to induce
apoptosis in some cancerous tissues but not in most healthy cells. In normal liver, TRAIL
has been shown to have very little apoptotic activity [25, 76]. This is also true for HCC cells.
However, when combined with other cytokines or chemotherapeutic agents, TRAIL can
induce apoptosis in HCC cells while having no detrimental effects in healthy hepatocytes.
Numerous studies have shown that a sensitizing agent is necessary to elicit an apoptotic
response from a TNF family ligand in HCC cells. Examples of such are type I and type II
interferons. These are pleiotropic cytokines known as antiviral agents with a role in
immunomodulation and inflammation. IFNy is a type II or immune interferon that has been
proven particularly effective in influencing and enhancing TRAIL activity [26, 27, 77].
Produced
by T lymphocytes
and natural
killer
(NK)
cells,
IFNy
is a potent
immunomodulator whose expression is induced by a unique set of stimuli [78]. Notably,
almost all cell types can come into contact with surrounding IFNy.
There are several hepatocellular carcinoma cell lines with a range of different states
of differentiation and similarities to primary hepatocytes [8]. Various studies have shown
(
28
_
that different HCC cell lines, have distinct expression profiles and respond differently to
different stimuli. These cell lines can be categorized according to expression of specific
genes and their phenotypic responses. Previous studies, in our lab and others, have
categorized HCC cell lines such as HepG2, FOCUS, Hep3B and Huh7, as well as primary
human hepatocytes. HepG2 cells are considered relatively differentiated (more epitheliallike and closer in expression and phenotypic behavior to primary hepatocytes), Hep3B and
Huh7 lines represent intermediate degrees of differentiation, and FOCUS cells are the least
differentiated of the group (more mesenchymal-like) [9-11]. Given the fact that Hep3B and
Huh7 cell lines are considered epithelial-like HCC cell lines with different degrees of
differentiation, both of these cell lines were examined to investigate their cell phenotypic
behavior in response to TNF family ligands, including TRAIL, in combination with IFN-y.
2.1.2
Chapter overview
Here, we explore two scientific inquiries: first, to determine the phenotypic
response of HCC cells to a few TNF family ligands in combination with IFN-y in terms of
apoptosis, total cell death and viability; second, to establish a cytokine stimulation regime
to produce different phenotypic behavior that could be correlated to microRNA expression.
Having information on cell phenotypic responses from multiple, well established platforms
allow us to have confidence in our results. With that in mind, in this chapter, we performed
three different cell death assays: flow cytometry, cell viability and hepatocyte cytotoxicity
by LDH activity in the media. These assays were performed to evaluate the extent of
apoptosis and total cell death induced on the HCC cell lines Huh7 and Hep3B under the
stimulus of the death receptors TNFa, FasL and TRAIL, in combination with IFNy. Flow
cytometry was used to measure activation of the apoptosis markers caspase 3 and PARP in
HCC cell lines under treatment with IFNy and a few member of the TNF family. Cell
viability was performed using trypan blue dye exclusion to investigate the effect of cell
density and TRAIL dosage on HCC cell lines. Finally, hepatocyte cytotoxicity by LDH activity
in the media was performed concomitantly with microRNA expression measurements on
Huh7 cells treated with different combinations of IFNy and TRAIL.
f
29
2.2
Experimental Procedures
2.2.1 Apoptosis response to death receptor ligands by measuring caspase 3 and
PARP using flow cytometry
To evaluate the apoptotic effects due to system perturbations of death receptor
ligands on HCC cell lines and primary human hepatocytes, HCC cells and primary human
hepatocytes were stimulated with varying combinations of recombinant human cytokines.
Resultant apoptosis effects were quantified by measuring the extent of caspase 3 activation
and the presence of one of its intermediate degradation targets: cleaved-PARP, using flow
cytometry. Huh7 and Hep3B cells (obtained from Prof. J.Wands, Brown University) were
seeded at a density of 3 x 104 cells/cm 2 in Eagle's minimal essential medium (ATCC),
supplemented with 10% fetal bovine serum (Hyclone) and 1% Penicillin/streptomycin
(Gibco). Primary human hepatocytes (from CellzDirect, now Live Technologies) were
obtained in a 6-well plate format with a human collagen/matrigel overlay in William's E
medium supplemented with dexamethasone, ITS+ (insulin, transferrin, and selenium
complex), antibiotics (gentamicin), L-Glutamine, and HEPES, per manufacturer's directions.
Samples were prepared by collecting floating and adherent cells. Cells were fixed with 4%
formaldehyde, then washed and permeabilized in 100% MeOH. Cells were kept at -200 C in
MeOH for one hour to up to 5 days before analysis. Fixed cells are stained with primary
antibodies against cleaved caspase 3 (clone C92-605; BD Biosciences) and cleaved-PARP
(clone F21-852; BD Biosciences) and secondary antibodies conjugated with AlexaFluor 350
(Invitrogen
A110460),
and
AlexaFluor
647
(Invitrogen
A21235),
respectively.
Measurements were performed on the BD LSR II Flow Cytometer and data analysis was
performed using the FlowJo software. Cells that are double negative for these substrates
were necrotic or alive at the moment of collection. Cells that are positive for both caspase 3
and PARP are classified as apoptotic.
To examine the effects of TNFa and trimeric Fas ligand (superFasL), the following
protocol was completed. Twenty four hours after seeding (or receiving in the case of
primary human hepatocytes) cells were serum starved and stimulated with IFNy (10
ng/mL, Peprotech) for 24 hours, followed by superFasL (100 ng/mL, Enzo Life Sciences) or
TNFa (100 ng/mL, PeproTech) for an additional 24 or 48 hours . Similarly, to evaluate the
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)
effects of recombinant TRAIL in monomeric or trimeric (SK-TRAIL) forms, cells were
serum starved and/or treated with 10 ng/mL of IFNy 24 hours after seeding,. Twenty four
hours later, cells were treated with 100 ng/mL of SK-TRAIL or different concentrations of
TRAIL (10, 100 or 300 ng/mL).
2.2.2 Cell viability in response to IFNy and TRAIL treatment
An alternative method to quantify the levels of cell death is to use cell viability
measurements such as dye exclusion and imaging. This technique is the standard method
to determine cell count and viability in academic and industrial research laboratories. The
Vi-Cell Cell Viability Analyzer (Beckman Coulter) was used to distinguish live and dead cells
by shape and dye exclusion. Live cells are rounded in shape and free of the dye (4% trypan
blue) thus appear clear in color. Dead cells have compromised membranes which enable
the transport of trypan blue dye, thus maintaining a dark appearance. Cell staining and
counting are done automatically, without the need for a hemocytometer. Unlike flow
cytometry, the Vi-Cell cannot distinguish which cells are apoptotic among all dead cells. It
does allow for quantification of live and dead cells in the population immediately after
sample collection. Vi-Cell yields appropriate measurement of cell count, cell growth (if
performed over time) and cell death. Cell viability was used for preliminary tests of system
variables and to determine a cell seeding and stimulus protocol conditions that would
optimize future experiments.
Two variables that may affect phenotypic responses include cell density and
cytokine concentration. The effects of initial cell density and dosage of cytokines were
tested in to determine treatments parameters appropriate for examining the correlations
between cell death and microRNA expression. In previous experiments, cells were seeded
at a density of 3 x 104 cells/cm 2 . It was unclear if cells would respond differently to the
cytokine treatments at lower or higher cell densities. The idea is that cell-cell interactions
and overall cell density may affect cellular responses. The purpose of changing cell density
and cytokine concentration was to observe various degrees of cell death in the system.
Cells were plated at 1 x 10s, 3 x 10s and 5 x 10s cells/well in 6-well plates. Each well has an
r
31
area of 9.6 cm 2, therefore the cell density that most closely resembles the previously used
density is 3 x 105 cells/well (3.125 x 104 cells/cm 2 ). Dosages of IFNy and TRAIL that were
examined ranged from 10 to 200 ng/mL. Cells were seeded, treated with IFNy 24 hours
after seeding, then treated with TRAIL 24 hours after IFNy treatment and collected 24
hours after TRAIL treatment for viability measurements. Table 1 shows the dose
equivalencies in ng per seeded cell for the treatment administered for Huh7 cells. Spaces
with NI indicated that these were not included in the experiment. In terms of concentration
per seeded cell, the highest dose for this particular experiment is at 1 x 10s cells per well
treated with 200 ng/mL of IFNy and 200 ng/mL of TRAIL.
Table 1: Cytokine doses for measurements of cell viability response
Cytokine
Dose (ng/mL)
Cell density
IFNy
IFNy
TRAIL
TRAIL
TRAIL
10
200
10
100
200
NI
4.OOE-03
Dose in ng/cell
(cells/well)
1.OOE+05
2.OOE-04 4.OOE-03 2.OOE-04
3.OOE+05
6.67E-05
5.00E+05
4.OOE-05 8.OOE-04 4.OOE-05
2.88E+05 *
6.94E-05
*This cell density corresponds to 3
NI= not included in study
2.2.3
NI
NI
x
NI
6.67E-04
NI
NI
NI
8.OOE-04
694E04
NI
104 cells/cm 2 in a 6-well plate with an area of 9.6 cm 2 .
Hepatocyte cytotoxicity measured by activity of LDH released in the media
Lactate dehydrogenase (LDH) is released in to the media when membrane integrity
is lost as hepatocytes die. The relative LDH activity is an indicator of cytotoxicity or total
cell death, which includes necrosis and apoptosis. The advantage of using cytotoxicity as
measured by LDH activity in the media, as compared to alternate cell death assays is that
the cells can still be collected and used for other measurements. Subsequently, samples
used for LDH activity assays were also used for microRNA measurements. After having
determined that Huh7 is a responsive HCC cell line and tested for different concentrations
of IFNy and TRAIL, it was possible to design a systematic approach to use different
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32
)
combination treatments to use cell death and microRNA measurement to find correlations
and mathematical models to tie the two together. Previously, the optimal cell density was
determined to be 3 x 104 cell/cm 2, while the optimal dosage of IFNy is 10 ng/mL and that of
TRAIL is 100 ng/ml. A new treatment in which cells are exposed to TRAIL first, and then
IFNy was included to elicit a treatment resulting in an intermediate cell death response.
In total there were six cytokine stimulation treatments (Table 2), including no
treatment (NT), INFy (I), TRAIL (T), INFy followed by TRAIL (I-T) and TRAIL followed by
INF-y (T-I). Each treatment was done in triplicate for each time point included. Medium is
collected from each well and the LDH activity is detected with the CytotoxOne assay
(Promega) at 48 and 72 hours following the initial cytokine treatment to quantify cell death.
Table 2: Cytokine treatments for cytotoxicity and microRNA measurements
First stimulus
24 hrs post-seeding
IFNv/TRAIL, ng/mL
--
IFNy 10
TRAIL 100
--
IFNV 10
TRAIL 100
Second stimulus
48 hrs post-seeding
IFNv/TRAIL, ng/mL
N/A
N/A
N/A
TRAIL 100
TRAIL 100
IFNy 10
Treatment
name
Treatment
abbreviation
No treatment
IFNy
TRAIL
No treatment-TRAIL
IFNV-TRAIL
TRAIL-IFNy
NT
'
Treatment
graph color
T
NT-T
I-T
T-I
This experiment was done concurrently with microRNA expression, which will be
discussed in more detail in Chapter 3. Cell death and microRNA expression were measured
concomitantly in order to be able to find mathematical relationships between the two.
Figure 3 shows experimental design pictorially for the purpose of clarity, for both
cell death and microRNA measurements.
33
Second stimulus:
First stimulus:
NT / IFNy / TRAILl
IFNy/ TRAIL
t=o
t=1
t =12
M
t=24
t=25
t=36
I
I
t=Time F(hours)
t=48
Ceildjeath
measured
24 hrs,
t=72
I
Cell death
measured
Global microRNA expression measured
Conditions
NT
0
NT 1
11
T 1
NT 12
112
T 12
NT 36
136
T 36
NT-T 36
l-T 25
T-I 25
NT24
124
T 24
NT-T 25
-T 25
T-1 25
NT 48
148
T48
NT-T 48
l-T 48
T-1 48
NT72
172
T 72
NT-T 72
l-T 72
T-1 72
Figure 3. Expe rimental design for a systematic approach to measure cell death and microRNA
expression.
NT=no treatment, I=IFNy, T=TRAIL. Numbers correspond to treatment duration in hours.
2.3
Results
2.3.1 Apoptosis response to death receptor ligands by measuring caspase 3 and
PARP using flow cytometry
The results indicate that both TNFa and trimeric Fas ligand (superFasL), in
combination with IFNy, were not sufficient to elicit an apoptotic response in primary
human hepatocytes, Huh7 and Hep3B cell lines (Figure 4.A). Apoptotic cells accounted for
less than 10% of the population of cells that were treated, which is not a sufficient amount
of apoptosis to consider for a treatment to be successful.
f 34
]
A.
14%
12%
0o 10%
'
8%
6%
* Hep3B
" Huh7
primary human
hel atocytes
L L-,LL-I
4%
2%
0%
a~a
..
W1
W)
~M.
*'d~
4.i.
N
CUN
-
B.
45%
(A
0.
40%
* Hep3B
35%
" Huh7
30%
25%
20%
15%
C6
CZ
OR
in.
10%
5%
0%
.
+
U6
Figure 4. Effect of TNF family ligands on apoptotic responses in primary human hepatocytes and HCC
cell lines.
A. Primary hepatocytes and HCC cell lines (Huh7 and Hep3B) treated with TNFa or superFasL at 100
ng/mL. B. HCC cell lines treated with different combinations of IFNy and super killer (SK)-TRAIL or
TRAIL (monomeric) at different concentrations.
Subsequently, the apoptotic effect of TRAIL in monomeric and trimeric forms and
for the effect of IFNy as a pre-sensitizing agent were tested (Figure 4.B). The trimeric
TRAIL, also known as Super Killer (SK)-TRAIL (Axxora) is an enhanced ligand that utilizes
the Killer' linker peptide mutated to increase disulfide-mediated cross-linking to form a
more stable oligomer by fusing the N-terminus of the extracellular domain of TRAIL (aa
35
- - -- -
- - -
95-281) to a His-tag and a linker peptide. The active multimeric conformation is stabilized
by an inserted mutation allowing an additional CC-bridge. Presumably, the conformation of
the trimeric TRAIL should facilitate ligation to TRAIL receptors, which exist as pre-ligand
trimmers [79]. However, the His tag on the linker peptide of SK-TRAIL can account for
some amount of cell toxicity, which may not occur through the TRAIL apoptotic pathway in
hepatocytes [76]. Treatments with only IFNy, SK-TRAIL or TRAIL resulted in less than 10%
apoptotic cells, and therefore fail to elicit a significant amount of apoptosis for both Huh7
and Hep3B cells. However, cells that were pre-sensitized with IFNy and then treated with
SK-TRAIL or TRAIL at concentration of 100 ng/mL or higher exhibited more than 20%
apoptosis.
Huh7 cells had the strongest response to the IFNy/TRAIL treatment at a
concentration of 100 ng/mL, with 43% of apoptotic cells. Both SK-TRAIL and monomeric
TRAIL were effective in eliciting an apoptotic response. The apoptotic response of TRAIL at
a concentration of 300 ng/mL was not significantly different than that of 100 ng/mL (t test
p=0.04), indicating that at this cell density, the saturating concentration of TRAIL is 100
ng/mL, with no added effect of increasing the dose. Given these results, Huh7 cells treated
with IFNy and TRAIL were used for other assays to elicit a robust apoptotic response and
to investigate other aspects of the system.
2.3.2
Cell viability in response to IFNy and TRAIL treatment
After having performed flow cytometry to look at the apoptotic response of two HCC
cell lines and primary hepatocytes, it was determined that the use of Huh7 cells would be
continued because they are more responsive to the cytokine treatment.
Figure 5 depicts the percentage of viable cells and at the difference in viability from
the no treatment control. To examine the significance of each response, a p-value was
determined using a two tailed Z-score test. Results show that the higher dose of IFNy and
TRAIL (1200/T200) produces a significant decrease in cell viability for the lower (1 x 105
cells/well) and higher (5 x 105 cells/well) cell densities, with p-values of 0 and 8.4 x 10-8,
respectively. However, these effects were not very pronounced (19% decrease in viability
for 1 x 105 cells/well and 15% for 5 x 105 cells/well). The highest amount of cell death was
36
)
observed for cells plated at 3 x 104 cells/ well at dosages of 10 ng/mL of IFNy, followed by
100 ng/mL of TRAIL, where there was a decrease in viability of 24%; and that it was not
significantly different from the next highest cell death result in which cells were treated at
200 ng/ml of both IFNy and TRAIL at a cell density of 1 x 105 cells/well (p-value of 3.5 x
10-2).
B.
A.
2
Cell density (cells/cm )
" 3.00E+05
Cell density (cells/well)
a 3.00E+05
100%
80%
* 1.00E+05
35%
* 1.00E+05
5.OOE+05
.1 30%
5.OOE+05
-- 25%
u
60%
-i
20%
15%
40%
*
C
20%
10%
I
5%
0%
-5%
0%
NT
110/T0
110/T200
1200/TO 1200/T2O
110/T
110/TOO
110fT200
1200/T10
1200/TZOO
110IbOO
Treatment (I=IFNy, T=TRAIL, ng/mL)
Treatment (I=IFNy, T=TRAL, ng/m )
C.
95%
90% 1
.us 85%
.0
*
Bo%
UIR75%
70%
y = -28.371x + 0.8641
R2 = 0.2481
65%
60%
0.E+00
1.E-03
2.E-03
3.E-03
4.E-03
5.E-03
ng TRAIL per cell
Figure 5. Cell viability determines cytokine concentration dosage and cell density conditions.
Cells were collected 48 hours after IFNy treatment and 24 hours after TRAIL treatment.
A. Percent of viable cells for Huh7 cells at different dosages of IFNy and TRAIL and different cell
densities. B. Difference from no treatment in percent of viable cells. C. Cell viability as a function of
TRAL concentration in ng per cell.
I=IFNy, T=TRAIL.
Figure 5.C illustrates the relationship between viability % and concentration of
TRAIL in ng/cell (Table 1). The correlation between these two variables was tested by
linear regression and Pearson correlation parameter. With a regression coefficient (R2) of
37
0.25 and a Pearson correlation coefficient of -0.498, it appears that the correlation between
cell viability and TRAIL estimated concentration in ng/cell is not strong enough to be
considered relevant. This result suggests that cell density and factors related to cell
proximity and sparseness play a role in cellular response to TRAIL induced apoptosis. Also,
it is possible that cells have reached saturation of the TRAIL receptor at a concentration of
6.67 x 10-s and don't seem to react to additional TRAIL.
These results indicate that cell density affect cellular responses to the cytokine
treatments administered. Even though the range of cell densities inspected here was not
extensive or broad, it is apparent that an intermediate sparseness was optimal for inducing
cell death in this system. Interestingly, the low and high cell density conditions failed to
elicit a significant amount of cell death. With this information, cell density was established
for future experiments at 3 x 104 cells/cm 2 ,as was done previously. However, there was
some inherent variability across biological samples. For example, the treatment with the
lowest percent of viable cells had 68% viability also had the higher standard deviation of
9%. Although this variability was small enough to not affect the distinct responses to the
different doses at different cell densities, it must be taken into account when using cell
death measurement to find mathematical relationships to microRNA expression.
Another relevant parameter to interrogate was TRAIL concentration. Changing
TRAIL dosage should affect cell viability and it is important to know at what concentration
of TRAIL saturation occurs. Cells were treated as explained previously, with an IFNy
concentration of 10 ng/mL and TRAIL concentrations of 0, 50, 100 and 300 ng/mL. Cells
were collected 18, 24 and 48 hours after TRAIL treatment. In this case, percentage of viable
cells was examined upon treatment (Figure 6.A). Viability percent depend on both total
number of cells and number of viable cells at the time of collection. To determine if there
was any indication of changes in cell growth that could account for changes in cell viability,
the number of total and viable cells was also examined (Figure 6.B and C). At 24 and 48
hours after TRAIL treatment, there appears to be no difference between doses of 100 or
300 ng/mL (p-values
0.3), meaning that saturation with TRAIL occurs at 100 ng/ml and
there is no added effect of increasing the dosage. Based on these results, TRAIL dosage was
set at 100 ng/mL for subsequent experiments.
f
38
)
Cell growth was monitored quantitatively over time, by measuring total number of
cells and viable cells at 0, 6, 12, 18, 24 and 48 hours after TRAIL treatment. The data
suggests that there is no detectable effect of treatment to overall cell growth, but rather,
there is a drop in cell viability after 24 hours, which is consistent with cell death caused by
TRAIL.
A
Percent of viable cells
100%
90%
0 ng/mL
1
M50 ng/mL
80%
U,
OJ
U
a,
70%
* 100 ng/mL
60%
* 300 ng/mL
50%
40%
*
30%
20%
10%
0%
18
24
48
Time (hours)
B.
C.
Total Cells
Viable Cells
0 ng/mL
1.00
SO ng/mL
0.90
ng/mL
-300 ng/mL
-100
0.80
*
50 ng/mL
0.80
-- 100 ng/mL
-300 ng/mL
0.70
x 0.70
E060
E
0-
*0 ng/mL
0.90
-
2~0.50
0
1- 0.30
0.30
.00.20
*S0.20
0.10
0.00
0
0.10
0.00
10
20
30
40
50
0
Time (hours)
10
20
30
Time (hours)
40
50
Figure 6. Cell viability for Huh7 cells treated with 1Ong/mL IFNy and different concentrations of
TRAIL.
A. Percent of viable cells at 18, 24 and 48 hours. B. Total number of cells per mL over time. C. Number
of viable cells per mL over time.
39
1
2.3.3 Hepatocyte cytotoxicity measured by activity of LDH released in the media
Results
from
the LDH
activity
assay are
consistent
with
previous
cell
death/apoptosis results (Figure 7). A treatment with 10 ng/mL of IFNy followed by 100
ng/mL of TRAIL resulted in the maximum amount of cell death, with an average of 15 % at
48 hours after the initial IFNy treatment (24 hours after TRAIL treatment) and 26% after
72 hours. The treatment in which the order of cytokine treatment is inverted (10 ng/mL of
IFNy followed by 100 ng/mL of TRAIL, 24 hours later) resulted in some lesser amount of
cell death at 72 hours, with an average of 16%. All other treatments resulted in less than 2%
cell death, which to the purpose of this study, amount to no cell death from the cytokine
treatment.
Statistical significance of the difference in LDH activity was evaluated by pairwise
comparisons using p-values obtained with t-tests. A statistically significant difference is
considered to have a p-value of less than 0.05. At 48 hours, IFNy-TRAIL treatment is
different from the single cytokine or no cytokine treatments, with p-values smaller than
0.05. Because there is a notable degree of biological variability in LDH activity for the IFNyTRAIL at 48 hours, the p-value for comparison to the TRAIL- IFNy does not quite meet the
criteria for a difference that is statistically significant (p-value=0.05). At 72 hours, for both
of the double cytokine treatment, the difference in LDH activity is statistically significant
from each other and from the LDH activity of the single cytokine treatments. Therefore, at
72 hours, there are three distinguishable levels of cell death, as measured by cytotoxicity.
This systematic approach to cytokine treatment of Huh7 cells that resulted in 3
distinct cell death response: low (no treatment or single cytokine), medium (TRAIL
followed by IFNy) and high (IFNy followed by TRAIL). It is important to note that there was
some amount of biological variability inherent in the cell death measurements, particularly
for the treatments that result in some amount of cell death. This variability was taken into
account when microRNA measurements were correlated with their corresponding cell
death results. This will be further discussed in chapter 4.
f
40
)
30%
25%
0 No treatment
20%
M IFNy
MTRAIL
15%
U No treatment-TRAIL
U IFNy-TRAIL
10%
TRAIL-IFNy
5%
0%
72 hours
48 hours
Figure 7: Different IFNy and TRAIL treatment in Huh7 cells cause different levels of cytotoxicity
at 48 and 72 hours
2.4 Discussion
Apoptosis can be induced systematically by different combinations of treatments
with different cytokines. In this project, HCC cells were treated with different cytokines,
including IFNy and some of the TNF family ligands. First, IFNy was used as a pre-sensitizing
agent in combination with other cytokines, namely TNAa, superFasL and SK-TRAIL
(trimeric). These cytokine combinations failed to elicit apoptosis in an effective way in both
Huh7 and Hep3B cells, as measure by flow cytometric analysis of caspase 3 and PARP
activation. Hep3B and Huh7 cells were tested for their responsiveness to these cytokines
for comparison, resulting in Huh7 having a higher level of cell death response. The
treatment that most optimally induces cell death was a combination of IFNy and
monomeric TRAIL in Huh7 cells. Thee three different assays used for measuring cell death,
flow cytometry, cell viability and LDH activity, show consistent results for Huh7 cells
treated with IFNy and TRAIL. Only the LDH activity assay was considered for the
computational analysis of cell death as a function of microRNA expression. Since this assay
only requires media from cells exposed to the treatment, it was possible to use the cell for
microRNA measurement concomitantly. There was a degree of biological variability arising
at different iterations (or biological replicates) of the experiment, which is not uncommon
41
for a cell line system. This variability was accounted for in other measurements and
computational analyses, discussed in more detail in chapter 4.
This experimental approach, in which a sensitizing agent precludes treatment with a
pro-apoptotic cytokine, can also be important in a physiological environment. Other tissues
may require a different combination of extracellular signals, but control of apoptosis is very
likely to happen in a similar multi-step fashion and it will be relevant to further understand
what happens at each step. By using this double cytokine treatment approach, it was
possible to elicit there different levels of cell death by playing with the order in which the
two cytokines, IFNy and TRAIL, are administered to the cells. Single cytokine elicited no cell
death response for up to 72 hours after the initial treatment. IFNy, followed by TRAIL
elicited the maximum amount of cell death, with a cytotoxicity of 26%. TRAIL followed by
IFNy resulted on an intermediate level of cell death response at a cytotoxicity of 16%. For
the purpose of this project, these cell death results provide a robust framework to have a
better understand the role of overall microRNA expression in cell phenotypic responses
and decision processes.
f
42
)
CHAPTER 3
3.
Measurements of microRNA expression changes induced by IFNy
and TRAIL in HCC cell lines
3.1
Introduction
3.1.1 MicroRNA expression in TRAIL and interferon signaling
The importance of microRNAs in cellular signaling pathways and cell phenotypic
responses is well established. Generally, microRNAs have systems wide effects by fine
tuning the expression of multiple targets and conferring robustness to biological processes,
rather than obliterating the expression of a single target [80]. MicroRNAs can also, in some
instances, act as a switch and elicit high levels of repression of their targets, depending on
the abundance of both the microRNA and the target mRNA [81] Various efforts have
succeeded in linking microRNA expression directly to phenotypic behavior, instead of
finding the effect of a single microRNA on one of its direct protein targets [12, 60]. TRAIL
and IFNy related pathways contain signaling proteins that are known targets of
microRNAs. In turn, several microRNAs are regulated by IFNy and TRAIL [32, 33, 64]. Two
good examples of microRNAs involved in TRAIL signaling are miR-221 and miR-222. In
non-small cell lung cancer (NSCLC), these two microRNAs were markedly up-regulated in
TRAIL-resistant and semi-resistant cells compared to TRAIL-sensitive NSCLC cells [30].
Type I and II interferons can significantly alter microRNA expression at short (<6 hrs) and
long (> 6 hours) time frames, which offers the possibility of using these cytokines for timedependent studies
of microRNA expression. For example, Yang, et al. recently
demonstrated that IFNa-induced up regulation of miR-21 is an early event (after 2 hours of
IFNa treatment) in a number of human cell lines from different tissues [64]. Another study
showed that IFN3 stimulation of Huh7 cells rapidly and strongly modulated the expression
of few microRNAs, among them miR-1 and miR-196, within 30 min [63]. On the other hand,
when a melanoma cell line was treated with IFNy, robust expression changes started to
emerge well after 12 h. Three microRNAs: miR-27a, miR-30a and miR-34a, had a delayed
f
43
regulation occurring at 72 h while none showed significant expression changes at early
time points between 30 min and 6 h [33].
It is possible that changes in microRNA expression that happen at different time
frames can play a role in cell sensitivity to TRAIL induced apoptosis. The question of time
dependencies in microRNA expression, in the context of their role in cell phenotypic
behavior has been, until now, poorly understood.
3.1.2
Platforms for expression profiling of microRNAs
Expression of specific microRNAs can be tied to regulation of different genes,
therefore, levels of expressions of specific microRNAs are relevant in cell phenotypic
behavior. Genome-wide analysis of microRNA expression has proven to be very relevant in
a number of biological contexts. Platforms for measuring mRNA and DNA have been
adapted to measure microRNA expression. However, there are several challenges in
measuring and analyzing microRNA expression data. For instance, microRNAs are much
smaller than their mRNA counterparts. Given that these oligonucleotides are so short in
length, the physicochemical properties and dynamics of hybridization to each microRNA
probe are highly heterogeneous. Primers designed to target microRNAs may need to use
the full microRNA sequence to be used as probes. There are hardly any "housekeeping"
microRNA genes, or ubiquitously expressed small RNAs that can be used as a reference for
normalization.
There are several platforms currently being used to measure microRNA expression.
The main ones include microarrays, quantitative PCR, multiplexed bead based assays and
next generation sequencing. Each of these platforms performs at different ranges of
number of samples and number of microRNAs that can be measured at once (Figure 8).
Since their sensitivity and specificity is dependent on very different parameters, it is not
uncommon to observe that measurements done on different platforms provide different
results. It becomes necessary to do multiple measurements across different platforms in
order to validate microRNA expression data [48, 49, 82]. Global expression data is useful in
finding expression patterns associated with a particular biological condition, whereas
f 44
)
expression of individual microRNAs is informative because there can be relevant
association between a particular microRNA and cellular behavior.
A
Massively parallel sequencing
(Illumina HiSeq/Genome Analyzer)
a
E
0~
4-
.,
E
0
a)
E
0
Number of samples per run
Figure 8: Comparison of different platforms for measuring microRNA expression in terms of
multiplexing and high-throughput ranges
(adapted from Luminex information material).
3.1.3
Chapter overview
Here we describe the measurement of microRNA expression by global profiling
using three different platforms: a bead based assay (Luminex ); microarray technology
using LNA probes (Exiqon), and next generation sequencing (Illumina). Additionally,
quantitative PCR, using LNA primers (Exiqon), was performed to validate some of the
sequencing results. Expression data from each of these studies was subjected to three
different analysis and visualization methods: hierarchical clustering, multivariate analysis
by means of PCA, and pairwise comparisons to find differentially expressed microRNAs by
means of DESeq [75]. PCA and hierarchical clustering were done using Matlab and DESeq
was performed in R.
----
r
45
Experimentally, the HCC cell lines Huh7 and Hep3B were treated with IFNy and
TRAIL and evaluated for global microRNA expression using a bead based assay. Huh7 cell
treated with IFNy and TRAIL were evaluated for global microRNA expression with
microarray technology. We found that microRNA expression patterns were associated with
different cytokine treatments and several microRNAs were identified as being differentially
expressed in cells treated with TRAIL.
3.2
Experimental Procedures
3.2.1
Global microRNA expression profiling using bead based assays on TRAIL
stimulated HCC cells
To evaluate the effects of TRAIL on microRNA expression in the short term, Huh7
and Hep3B cells were treated with 10 ng/mL of IFNy for 24 hours, followed by 100 ng/mL
of TRAIL for 1 hour. Cells were seeded at a density of 3 x 104 cells/cm 2 in 10 cm plates,
cultured in Eagle's minimum essential media (EMEM) supplemented with fetal bovine
serum. Media with serum was substituted with serum-free media upon treatment with
IFNy. RNA extraction was done using Trizol, following manufacturer's instructions. RNA
was quantitated using the Nanodrop instrument. The quality of the total RNA was verified
by an Agilent 2100 Bioanalyzer. Samples with an RNA integrity number (RIN) of less than 7
were discarded. Total RNA was used to do global expression profile using the Flexmir beadbased assay (Luminex). Samples were prepared according to manufacturer instructions. All
samples loaded contained 0.5 ptg total RNA. This assay was used to measure 320
microRNAs, annotated in miRBase v 8.0. This instrument can measure up to 100 analytes at
the same time. The samples were divided into 5 pools, with each pool containing beads to
measure up to 65 different microRNAs. Bead signal was read using the BioPlex 200 (BioRad). Signal is reported as medium fluorescence intensity (MFI), which is an indication of
the relative amount of microRNA detected. In this assay the signal for miR-363-5p was very
noisy, and therefore it was not accounted for in the analysis. The data was quantified by
using a sample with known femtomolar quantities of microRNA (Ambion) to construct a
standard curve and estimate absolute microRNA abundance.
f
46
)
3.2.2 Global microRNA expression profiling using microarray technology on TRAIL
stimulated HCC cells
Huh7 cells were treated as described in 3.2.1. The quality of the total RNA was
verified by an Agilent 2100 Bioanalyzer profile. One ptg total RNA from sample and
reference was labelled with Hy3TM and HyS'
fluorescent label, respectively, using the
miRCURYTM LNA Array power labelling kit (Exiqon) following the procedure described by
the manufacturer. The Hy3T"-labeled samples and a Hy5M'-labeled reference RNA sample
were mixed pair-wise and hybridized to the miRCURYT M LNA array version 11.0 (Exiqon),
which contains capture probes targeting all human microRNAs registered in the miRBASE
version 13.0 at the Sanger Institute. The hybridization was performed according to the
miRCURYT" LNA array manual using a Tecan HS4800 hybridization station (Tecan). After
hybridization the microarray slides were scanned and stored in an ozone free environment
(ozone level below 2.0 ppb) in order to prevent potential bleaching of the fluorescent dyes.
The miRCURY" LNA array microarray slides were scanned using the Agilent G2565BA
Microarray Scanner System (Agilent Technologies, Inc.) and the image analysis was carried
out using the ImaGene 8.0 software (BioDiscovery, Inc.).
3.2.3 Analysis for bead based assay and microarray expression data
Expression data from the bead based assay was normalized to the average
expression across samples. For microarrays data, the quantified signals were background
corrected (Normexp with offset value 10 -[83]) and normalized using the global Lowess
(LOcally WEighted Scatterplot Smoothing) regression algorithm. Global expression data
from these two assays were analyzed using hierarchical clustering, principal component
analysis and DESeq. PCA was performed using the princomp function in Matlab. Prior to
PCA the data was mean centered and variance scaled. For hierarchical clustering, the log
base 2 of the fold change of expression was calculated prior to analysis. It was performed
using the clustergramfunction in Matlab. Differential expression analysis was carried using
the DESeq package for R.
47
3.2.4 Global microRNA expression profiling using next generation sequencing on
Huh7 cells systematically treated with IFNy and TRAIL.
Next generation sequencing has proven useful for generating quantitative and
extensive data sets from gene expression experiments and is well suited for global
expression profiling of microRNAs in this study. Samples were prepared as explained in
section 2.2.3.
Figure 3 shows a schematic of the experimental design and Table 3 indicates all
conditions included in this study. Total RNAs was extracted from 93 different samples,
corresponding to three sets of biological replicates from each of the 31 conditions listed in
table 3. Each set of treatment conditions come from cells harvested on different dates from
different passage number, for a total of 3 biological replicates for each treatment. RNA was
extracted using Trizol, following manufacturer instructions. Total RNA from each sample
was quantitated using the Nanodrop instrument. The quality of the total RNA was verified
by an Agilent 2100 Bioanalyzer. All samples analyzed had a RIN number higher than 8, so
they were all adequate for sequencing analysis. The RNA was processed and prepared
using the TruSeq Small RNA Sample Prep Kit from Illumina, according to manufacturer
instructions. The samples were barcoded with the purpose of pooling together 11 to 13
samples per lane on a flow cell in the Illumina HiSeq 2000 instrument at the BioMicro
Center.
3.2.4.1 Data analysis for Illumina sequencing
The raw data from sequencing was processed to find known microRNA sequences,
annotated in miRBase v 18.0. This was done at the BioMicro Center and the Fraenkel lab.
The resulting data contained counts of each microRNA. The data was processed so that the
resulting microRNA expression was accounted for in reads of individual microRNA per
million of total reads. MicroRNAs with less than 100 million reads across all samples were
discarded. Further data analysis and modeling will be discussed in chapter 4.
f
48
J
Table 3: List of treatments for microRNA expression measurements using a systematic approach
Treatment 1, 24 hrs
Treatment 2, 48 hrs
Total
Treatment
Treatment
[IFNy/TRAIL]
[IFNy/TRAIL]
duration
name
#
ng/mL
ng/mL
(hrs)
0
0
NA
0
NT 0
NA
12
NT 1
I10
NA
12
11
T 100
NA
12
T1
0
NA
12
NT 12
I 10
NA
12
I 12
T 100
NA
12
T 12
0
NA
24
NT24
I 10
NA
24
I 24
T100
NA
24
T 24
0
T 100
25
NT-T 25
I 10
T100
25
I-T 25
T 100
I 10
25
T-I 25
0
NA
36
NT36
I 10
NA
36
I 36
T100
NA
36
T36
0
T 100
36
NT-T 36
I 10
T100
36
I-T 36
T 100
I 10
36
T-I 36
0
NA
48
NT48
110
NA
48
I48
T100
NA
48
T 48
0
T 100
48
NT-T 48
I 10
T100
48
I-T 48
T 100
I 10
48
T-I 48
0
NA
72
NT 72
110
NA
72
172
T 100
NA
72
T72
0
T 100
72
NT-T 72
I 10
T100
72
I-T 72
T 100
I 10
72
T-I 72
I
49
3.2.5
Quantitative PCR using LNA probes
Quantitative PCR was performed on a few of the samples that were subjected to
Illumina sequencing. The treatment regime is explained in Figure 3 and table 3. The Exiqon
MiRCURY LNA" Universal RT kit was used for this purpose. cDNA synthesis was done
according to the manufacturer's instructions and qPCR was conducted with a Chromo 4
thermal cycler and PCR system (Bio-Rad). The amplification curves were individually
inspected for quantification. A standard curve was constructed from a sample with known
femtomolar concentration of microRNAs being analyzed, to get an estimate of absolute
quantitation.
3.3
Results
3.3.1
Global microRNA expression profiling using bead based assays on TRAIL
stimulated HCC cells
The Flexmir kit for the Luminex bead-based assay includes probes for 320
microRNAs. Expression data is reported as median florescence intensity (MFI) for each
microRNA probed. Data from this experiment was analyzed as explained in 3.2.3, using
PCA and hierarchical clustering (Figure 9 and Figure 10). In addition, a MFI plot of TRAIL
treated vs non treated cells was used for comparing overall trends in Huh7 and Hep3B cells.
These analysis and visualization methods show microRNA expression patterns associated
with the different TRAIL treatments.
The loading vectors in principal component space indicate that PC1 separates the
data according to TRAIL treatment status and PC2 separates HepB and Huh7 cells that
were treated with TRAIL (Figure 9). However, it is not clear that there is a distinct
expression pattern associated with Huh7 cells that are not treated with TRAIL. The MFI
scatter plot and hierarchical clustering were used as visualization tools to qualitatively
evaluate general trends in microRNA expression patterns. Both of these graphs show that
for Huh7, there is an overall pattern of down-regulation of microRNAs within one hour of
TRAIL treatment, while Hep3B shows a general up-regulation trend.
(50
)
Hierarchical clustering of microRNA expression data illustrates a similar story as
PCA analysis. In this case, the expression data is transformed by calculating the log2 of the
ratio of expression of each microRNA over the average expression of the entire population
represented in the data set. Clustering the data correctly classifies the different TRAIL
conditions. Once again, Huh7 exhibit a general pattern of microRNA down-regulation and
Hep3Bs exhibit a general pattern of up-regulation of microRNAs.
A.
HuH7
no TRAIL
0.80.60.4M
0.2-
Hep3B.
Hep3B
TRAIL
TRAIL
0
He...:.
*
.
Hep3B
*~-....aHep3B
no TRAIL
2
.no TRAIL
e -0.2HuH7
TRAIL
1
-0.4-
-0.6-0.8 -
-0
8
HuH7
no TRAIL
2
HuH7
TRAIL
2
IIII
-0.8
I
-0.6
-0.4
-0.2
I
0
0.2
PC 1
25.4% variance explained
I
0.4
0.6
0.8
B.
MFI of miRNAs for
TRAIL treated vs IFNg only treated cells
1000.00
yv
900.00
.9988x - 20.663
R, 0,8001
800
700,00
+
+
60.00
-500.00*
o400.00
2
9
y
*
300.00
200.00
*
.. 4 .:
100.M
0.00
100.00
200.00
300.00
400.00
500.00
600.00
5
97
21 67
F. . - ..
2_ 7
R z 0O.742
9
*
700.00
800.00
900.00 1000.00
MFI NO TRAIL
+ Huh7
* Hep3B
Figure 9. Global microRNA expression of Huh7 and Hep3B measured with the Luminex bead based
assay.
A. PCA biplot of microRNA expression in Huh7 and Hep3B cells by the Luminex bead based assay. B.
MFI plot of microRNA expression of Huh7 and Hep3B cells with and without TRAIL treatment.
f
51
]-
A.
1
0
-1
I lnel IHu7
IFNy
TRAIL_
24hrs
_
Hep3B
Hep3B
Huh
it
24hrs
24hrs
24hrs
24hrs
24hrs
24hrs
24hrs
_
lhr
ihr
1hr
hr
B.
Hu '7
lr
p3
C.
Cell line
HuH7
HuH7
HuH7
HuH7
IFNy
24hrs
24hrs
24hrs
24 hrs
TRAIL
Ihr
IFNy
Ihr
TRAL
I
24hrs
24hs
24hrs
-
1
1
r
24hr
1hr
Figure 10: Hierarchical clustering of global microRNA expression of Huh7 and Hep3B measured with
the Luminex bead based assay.
A. Hierarchical clustering for the two HCC cell lines. B. Huh7 cell. C. Hep3B cells. Columns correspond
to different TRAIL treatments or Huh7 cell passages, and rows correspond to different microRNAs.
Legend is scaled in log2 of individual microRNA expression over the population average.
1
52
)
Differential expression analysis was performed for pairwise comparisons. There
were a few microRNAs that were differentially expressed between conditions and across
cell lines (Table 4). Four microRNAs were differentially expressed between Huh7 and
Hep3B cells not treated with TRAIL, six for Hep3B cells treated with TRAIL and four for
Huh7 cells treated with TRAIL. There was no overlap among the microRNAs differentially
expressed upon treatment with TRAIL for both cell lines. When looking at the microRNAs
that are differentially expressed between cell lines we see that there is very little overlap
between differentially expressed microRNAs in Huh7 and Hep3B cells. Interestingly, miR10a and miR-362 are expressed in Hep3B cells upon TRAIL treatment and are also
expressed in Huh7 cells not treated with TRAIL. The fact that Hep3B cells are less
susceptible to the apoptotic effects of TRAIL, suggests that there is a correlation between
loss of expression of these microRNAs and cell death.
Table 4.A-C: Differentially expressed microRNAs, measured by Luminex bead based assay
to look at differences between cell lines and changes induced by TRAIL
A.
Pairwise comparison between cells lines with no TRAIL treatment
miRNA
miR-181a
miR-432
miR-10a
miR-362
B.
Mean
5683.69
25835.56
3452.18
1785.86
Huh7
0.00
45427.82
6904.35
3571.73
pval
1.62E-02
2.61E-03
4.32E-02
1.62E-02
Pairwise comparison of Hep3B cells with and without TRAIL treatment
miRNA
Mean
miR-544
miR-218
miR-514
miR-362
miR-453
miR-10a
4229.62
3613.42
3313.43
6582.58
4658.53
3801.14
C.
Hep3B
11367.39
6243.29
0.00
0.00
Hep3B No
TRAIL
8459.23
7226.85
6626.86
0.00
0
0
Hep3B
TRAIL
0.00
0.00
0.00
13165.17
9317.06
7602.29
pval
3.61E-05
8.27E-06
5.43E-06
3.11E-03
2.04E-02
2.94E-02
Pairwise comparison of Huh7 cells with and without TRAIL treatment
miRNA
miR-106a
miR-18b
miR-452
miR-330
Mean
10391.34
10383.38
9400.49
2235.63
Huh7
No TRAIL
20782.67
20766.76
18800.97
4471.25
Huh7
TRAIL
0.00
0.00
0.00
0.00
pval
3.21E-15
3.84E-03
1.90E-02
3.77E-03
f
53
1
3.3.2
Global microRNA expression profiling using microarray technology on TRAIL
stimulated HCC cells
Microarray technology was used to further look at expression of microRNAs in Huh7
cells treated with TRAIL.
A.
HuH7 p16
no TRAIL
4
HuH7 p1
no TRAIL
0.6
0.4
F
0.2
F-
HuH7 p14
TRAIL
'~~~**
-.
4.
-0.2
-
,'
-0.4
-
-0.6
-
-0.6
-0.2
-0.4
**
.**
p16
TRAIL
SuH7
~
0
.
.*
0.2
0
0.4
0.6
PC 1
49.4% variance explained
B.
I
1
I
r--1
I
I
0.5
0
-0.5
Passage
number
14
16
16
14
IFNy
24 hrs
24 hrs
24 hrs
24 hrs
1 hr
1 hr
TRAIL
Figure 11: Analysis of microRNA expression data from Exiqon microarrays.
A. Principal component analysis of cell line expression data B. Hierarchical clustering of expression
from Huh7 samples. Columns correspond to different TRAIL treatments or Huh7 cell passages, and
rows correspond to different microRNAs.
-1
54
Similarly to the bead based assay, there was a pattern of general microRNA downregulation upon treatment with TRAIL in Huh7 cells, as shown with hierarchical clustering
(Figure 11.B).
Overall there were 46 microRNAs differentially expressed in Huh7 cells
treated with TRAIL (Table 5). There was no overlap between microRNAs differentially
expressed found with the bead based assay and microarray technology.
Table 5: Differentially expressed microRNAs, measured by Exiqon microarrays to look at expression
changes induced by TRAIL
miRNA
miR-20a*
miR-624
miR-32
miR-101
miR-33a
miR-422a
miR-320b
miR-665
miR-1281
miR-320d
miR-620
miR-574-3p
miR-320c
miR-15b
miR-637
miR-197
miR-92a
miR-1265
miR-1246
miR-20b*
miR-34b
miR-1308
miR-320a
miR-483-3p
miR-886-5p
miR-574-5p
5SrRNA-5
miR-765
miR-92b
miR-886-3p
miR-642
miR-1827
miR-1237
let-7c
let-7e
miR-1255a
miR-483-5p
miR-423-5p
miR-668
miR-519e
miR-340_s
miR-1290
miR-647
miR-1826
miR-300
miR-1285
Mean
831.86
735.95
888.43
815.48
888.86
811.46
1463.74
1351.36
2306.35
1416.17
1157.77
1074.00
1570.28
1355.74
1143.93
1023.58
1270.72
1214.50
1530.45
1104.55
1186.76
1056.22
1527.72
1693.68
880.32
1304.66
1467.09
1404.26
1306.79
1283.48
1420.89
1314.05
1562.66
1351.60
1323.24
1429.43
1458.81
1307.45
1137.26
1174.69
1281.50
2027.26
1225.35
1332.20
1502.47
1777.36
Huh7 No TRAIL
1127.36
1002.79
1234.35
1137.22
1277.39
1281.53
964.00
886.88
1507.88
922.86
740.95
683.97
999.26
859.66
722.99
639.23
792.91
756.39
947.92
677.86
724.15
641.52
925.92
998.72
517.01
765.21
860.16
822.86
759.67
739.76
818.45
755.78
881.82
756.70
739.74
760.40
767.85
685.21
581.17
587.72
631.67
980.54
578.30
552.51
586.12
521.91
55
Huh7 TRAIL
536.36
469.10
542.51
493.74
500.33
341.40
1963.47
1815.84
3104.82
1909.48
1574.58
1464.02
2141.30
1851.82
1564.86
1407.93
1748.52
1672.61
2112.97
1531.25
1649.37
1470.93
2129.52
2388.64
1243.63
1844.11
2074.03
1985.66
1853.91
1827.20
2023.32
1872.31
2243.49
1946.50
1906.74
2098.46
2149.76
1929.70
1693.35
1761.67
1931.33
3073.99
1872.39
2111.89
2418.81
3032.82
I
Log 2 (fold
change)
-1.07
-1.10
-1.19
-1.20
-1.35
-1.91
1.03
1.03
1.04
1.05
1.09
1.10
1.10
1.11
1.11
1.14
1.14
1.14
1.16
1.18
1.19
1.20
1.20
1.26
1.27
1.27
1.27
1.27
1.29
1.30
1.31
1.31
1.35
1.36
1.37
1.46
1.49
1.49
1.54
1.58
1.61
1.65
1.69
1.93
2.05
2.54
pval
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.03
0.00
0.00
0.02
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Although the general trends in microRNA expression in Huh7 cells treated with
TRAIL were similar when examined with the bead based assay and microarray technology,
quantitative measurement individual microRNA expression and differential expression
analysis were vastly different.
3.3.3 Global microRNA expression profiling using next generation sequencing on
Huh7 cells systematically treated with IFNy and TRAIL
A lot of information can be derived from next generation sequencing of microRNAs.
This is particularly true of the study presented here, because of the systematic approach
used to treat cells and couple microRNA expression to treatment time and cell phenotypic
behavior. In this section, the focus is on the microRNA expression data across treatments
and time points. Further analysis of this data, incorporating computational modeling and
data on cell death, will be discussed in chapter 4. A piece of information that is relevant to
this section, but explained in more detail in chapter 5 is the finding that at 25 hours, there
microRNA expression data has more cell death predictive power. Here, microRNAs that are
highly abundant and differentially expressed at 25 hours are examined.
Small RNAs were isolated from 93 different samples, corresponding to three sets of
biological replicates from each of the 31 conditions listed in Table 2. Each set of treatment
conditions was collected on different dates. Of the 93 samples subjected to sequencing,
data from 20 of the samples were discarded because they did not contain sufficient reads
across all microRNAs and therefore did not meet quality control criteria for further
analysis. As a result, 30 different cytokine conditions are represented by 2 or 3 biological
replicates, except for the condition with TRAIL-IFNy at 72 hours, which did not meet
quality control criteria and was not included in the analysis. MicroRNAs with a maximum
expression level of less than 100 reads per million were removed. The resulting data matrix
contains information on 168 microRNAs for 73 samples representing 30 cytokine
conditions. Out of those microRNAs, 98 are listed in the high-confidence list in miRBase
[41]
(56
Several data analysis and visualization approaches can be used on this data set to
answer different questions about biological processes. First, to visually inspect the data,
we subjected it to hierarchical clustering (Figure 12). There is a noticeable difference in
overall microRNA expression patterns 36 hours of the initial treatment for the IFNy-TRAIL
treatment, which produces the maximum amount of cell death. Expression profiles for this
cytokine treatment at 48 and 72 hours are clustered close to the 36 hour profile. These
results indicate that microRNA expression patterns are related to cytokine treatment and
cell death. Interestingly, at 72 hours, the expression profile for the TRAIL-IFNy treatment,
which induces an intermediate amount of cell death, clusters closely to the IFNy-TRAIL late
time points conditions.
Further data analysis, discussed in more detail in chapter 4 revealed that the 25
hour time point is highly predictive of cell death, and therefore a relevant time point to look
at in more detail. In this section, the result of the most abundant microRNAs and those that
are differentially expressed at 25 and 36 hours are presented (Table 7).
Figure 12: Hierarchical clustering of microRNA expression data from Illumina sequencing.
Columns correspond to different treatments and rows correspond to microRNAs. There are 168
microRNAs that were found to be expressed at detectable levels.
NT=no treatment, I=IFNy, T=TRAIL. Numbers correspond to treatment duration in hours.
--
57
3.3.3.1 Abundance of microRNAs
Absolute abundance of microRNAs is directly related to their ability to repress
translation.
Changes in microRNA expression can be informative of cell phenotypic
response, while microRNA abundance is informative of which microRNAs might be directly
involved in target repression [81]. For the purpose of examining the microRNAs more
abundantly represented in the population, percentage of microRNA representation was
calculated by dividing the number of reads of each microRNA to the total number of reads
detected for that particular sample. The top ten abundant microRNAs are listed in Table 6
and their expression patterns are shown in Figure 13. MicroRNAs were ranked according
the average of the percent of representation in the population across all samples and the
percentage of expression that they represent in the population, at 25 and 36 hours of
treatment, since these time point appear to be the most informative of treatment and cell
death status in the data set.
Table 6 also includes the percent of change in expression when comparing
expression in the IFNy-TRAIL treatment to no treatment at t = 0 hours, no treatment or
IFNy only at the corresponding time points. Interestingly, the top 10 most abundant
microRNA were the same for the average representation, and at 25 and 36 hours after the
initial treatment. Three of these microRNAs, miR-10b-5p (down), miR215 (up) and miR92a-3p (up), undergo changes greater than 25% from t = 0 to t= 25 hours.
Other
microRNAs detected in high abundance (in the top 20) in this data set include miR-21-3p,
and miR-92b-3p, which are also differentially expressed at 25 hours. The complete set of
result for abundance and relative rank is presented in appendix B.
Expression patterns from the most abundant microRNA reveal changes that are
both time and treatment dependent. Two noteworthy examples, mir-192-5p and miR-1225p, are highly abundant in these cells, but for the most part, changes in expression do not
correlate to cytokine treatment or cell death. In other experiments, the levels of these two
microRNAs were perturbed using inhibitors (sponges) and mimics to evaluate their effect
on cell death. The sponges and mimics were effective in changing the activity of these two
microRNAs, but did not elicit any changes in cell responsiveness to IFNy and TRAIL. These
results are illustrated in Appendix A.
58
J
Table 6: Ten most abundant microRNAs detected by next generation sequencing
% change at
25 hours
average
representation
in the population
miRNA
% change at
36 hours
NT
0NTt0
NT, t = 0
NT, t = 24
1,t = 24
0
NT, t =
hours
NT, t = 36
1, t = 36
17.99%
8.41%
hours
miR-10b-5p
22.50%
-33.52%
-31.25%
miR-192-Sp
18.81%
6.23%
-2.19%
-4.84%
5.20%
-4.46%
-5.87%
miR-215
6.42%
62.19%
66.69%
47.32%
24.82%
-28.86%
-25.18%
miR-148a-3p
6.30%
16.18%
0.32%
16.14%
11.37%
-22.15%
-0.75%
miR-21-5p
6.28%
13.73%
46.51%
2.21%
46.06%
80.77%
37.02%
miR-22-3p
5.47%
-4.79%
12.40%
10.78%
-22.40%
-13.32%
-10.96%
miR-191-5p
3.72%
-19.61%
-19.74%
-23.00%
-30.22%
-25.71%
-12.38%
miR-92a-3p
3.40%
56.02%
58.95%
59.73%
38.13%
11.38%
3.50%
miR-122-5p
2.62%
7.57%
-28.05%
-3.36%
-16.81%
-42.20%
-26.84%
miR-27b-3p
1.93%
17.12%
15.19%
-2.87%
38.97%
18.92%
13.36%
B
A
miR-27b-3p
354X
-25.99%
-24.02%
C
4
D
miR-122-Sp
1
10 miR-191-5p
4miR-92a-3p
3
3
4
4
2.5
2
2
1.5-
0
12
24
36
48
60
10
72
E
3
12
24
36
48
60
72
20
F
7
14
12
24
36
48
60
G
miR-22-3p
4.0
miR-21-5p
20
72
12
24
36
48
60
72
60
72
H
OX
10X04
104miR-148a-3p
MIR-21S
6
8
8
8
6
6
5
4"
0
12
24
36
48
60
72
miR-192-5p
2.5X10
40
12
24
36
48
60
0
72
miR-10b-5p
3.
24
36
48
60
72
0
12
24
36
48
+NT
a*IFNg
-*TRAIL
3.
2
2.5
1.5
*NT-TRAIL
-TRAIL-IFNg
2-
0
12
12
24
36
48
60
72
50
12
24
36
48
60
72
Figure 13: Expression patterns of ten most abundant microRNAs across all conditions, detected by
next generation sequencing
59
3.3.3.2 Differentially expressed microRNAs
Differentially expressed microRNAs were identified using the DESeq package for R,
doing pairwise comparisons across conditions and time-points.
Table 7 includes the
microRNAs that are differentially expressed at 36 and 25 hours, which are two very
relevant time points. This method is based on the negative binomial (NB) distribution, with
variance and mean linked by local regression. The DESeq model algorithm allows general,
data-driven relationships of variance and mean for fitting the model to data [75]. Several
microRNAs were identified as differentially expressed and strongly correlated to cell death.
This correlation will be discussed in more detail in chapter 4. The expression patterns of
those microRNAs that were differentially expressed at 25 hours are shown in Figure 14
Table 7: Differentially expressed microRNAs at 25 and 36 hours.
25 hours
mi R-1246
miR-146a-5p
miR-18la-3p
let-7c
miR-10b-5p
miR-21-3p
miR-615-3p
miR-29a-3p
miR-210
miR-92b-3p
miR-128
miR-5701
1et-7a-5p
miR-106b-3p
miR-1180
miR-1246
miR-125b-2-3p
miR-1273g-3p
miR-128
miR-151a-5p
miR-181a-3p
mi R-1908
miR-193a-3p
miR-193a-5p
miR-196b-5p
miR-19a-3p
36 hours
miR-19b-3p
miR-210
miR-21-3p
miR-221-3p
miR-23b-3p
miR-29a-3p
miR-30d-3p
miR-3129-3p
miR-331-3p
miR-335-3p
miR-3656
miR-378a-3p
miR-3960
miR-424-5p
miR-4449
miR-4455
miR-4485
miR-4488
miR-4492
mi R-4516
miR-4532
mi R-4792
miR-483-5p
miR-5701
miR-574-3p
miR-577
miR-589-5p
miR-664-3p
36 hrs across conditions
mi R-1246
miR-146a-5p
miR-1273g-3p
miR-196b-5p
miR-19a-3p
miR-423-5p
miR-3656
miR-92b-3p
miR-3960
miR-375
miR-4449
miR-615-3p
miR-4485
miR-200b-3p
miR-4488
miR-125b-5p
miR-4492
miR-216b
miR-4516
miR-4532
mi R-4792
miR-193a-5p
miR-19b-3p
In general these microRNAs follow different expression patters along the course of
the treatment. Let7-c, miR-10b-5p and miR-181a-3p are down-regulated and remain down
for the rest of the treatment course; miR-146a-5p goes down and then up; miR-92b-3p,
miR-128 and miR-615-3p go up and then down, while miR-21-3p, miR-29a-3p, miR-5701,
miR-1246 and miR-210 are up-regulated and remain relatively high in expression.
(
60
)
A
B
let-7c
C
3510,
150
.5 x j
100
D
miR-181a-3p
miR-10b-5p
0
1
miR-146a-Sp
600
6000
400.
4000
200
2000
25
50
2
0
12
24
36
48
60
1.0
72
E
12
24
36
48
60
F
miR-128
10000
1000
200
5000
500
24
36
48
60
0
12
24
36
48
60
miR-29a-3p
400
600
300
400
200
24
36
48
60
12
72
0
72
12
24
36
48
60
72
X104
miR-21-3p
24
36
48
60
72
1000
0.
0
12
24
36
48
60
72
60
72
L
miR-5701
miR-1246
miR-210
1000
1000
500
12
60
K
800
0
40
72
i
1000
48
10
72
I
36
miR-615-3p
300
12
24
H
1500
0
12
G
miR-92b-3p
15000-
0
72
800
500
600
400
12
24
36
48
60
72
'0
12
24
36
48
60
72
20%
12
24
36
48
+ NT
-*IFNg
-TRAIL
*
NT-TRAIL
*TRAIL-IFNg
+IFNg-TRAIL
Figure 14. Expression of microRNAs differentially expressed at after 25 hours of IFNy treatment and 1
hour of TRAIL treatment.
NT=no treatment, I=IFNy, T=TRAIL. Numbers correspond to treatment duration in hours.
(61
3.3.4 Quantitative PCR
Quantitative PCR was done to validate microRNA expression for the microRNAs
differentially expressed microRNAs and other relevant microRNAs. A few relevant samples
were selected for this study. Out of the microRNAs measured, 6 microRNAs were validated
using qPCR: miR-19a, miR-21-3p, miR-29a, miR-210, miR-1246, and let-7c. Validation was
determined by comparing the log base 2 of the fold change over the reference sample (no
treatment at t=O hours) for both sequencing and qPCR. Given that different measurements
from platforms result in different results for microRNA expression, we seek to find if the
fold changes were positive or negative for both qPCR and sequencing in the selected
samples.
The miRNAs included in Figure 15 validated the sequencing data, as evidenced by
parallel fold changes in the same direction, particularly for samples of the IFNy-TRAIL
treatment at 25 and 36 hours. These results provided information regarding the upregulation and down-regulation of the differentially expressed miRNAs reported by next
generation sequencing.
miR-19a
s
miR-21
miR-29a
4
-3
-
2
2 6 45
78
12345678
910 11 1213
910111213
11213
1234567910
-3
-4
.5
miR-1246
5miR-21O
S4
let-7c-1
3
11
,0_diLddd~kLiih1
2
3 4
~~~~~1
2 3 4
3
5
6
7
8
9 10 11
5
6
7
8
9
10 11 12
12
13
12
34
S
6
7
910
112
13
3
-4
26
Sample legend
1 NT-0
2 NT-24
5NTT-25
6 IT-25
91-36
10 T-36
3 1-24
7 TI-2
4 T-24
8 NT-36
11 NTT-36
12 rT-36
IluiaSqecn
13 1n-36
M
qPCR
Figure 15. Quantitative PCR on microRNAs differentially expressed at 25 hours after initial treatment.
NT=no treatment, I=IFN'y, T=TRAIL. Numbers correspond to treatment duration in hours.
62
3.4
Discussion
Global expression profiles of microRNA were very informative of how expression
patterns change upon treatment with the cytokines IFNy and TRAIL in HCC cell lines. These
results were also informative about the inherent differences arising from performing
expression measurements on different platforms.
MicroRNA expression was evaluated for Huh7 and Hep3B cells with the bead based
Luminex assay. The Exiqon microarray platform was used to further examine the effects of
1 hour of TRAIL treatment on Huh7 cells sensitized with IFNy. Interestingly, Huh7 cells
treated with TRAIL showed patterns of overall microRNA down-regulation in all platforms
used for microRNA measurements. Although general patterns of expression of microRNAs
in Huh7 cells were similar from both Luminex and microarrays profiles, levels of
expression of individual microRNAs were rarely reproducible.
A systematic approach to study dynamic microRNA expression changes and cell
death was implemented. Global expression profiles were generated using next generation
sequencing on the Illumina platform. The most distinct expression pattern was observed
for the IFNy-TRAIL treatment, 36 hours after the initial treatment. This treatment also
displayed distinct expression patterns at 48 and 72 hours. Some of the most abundant
microRNAs present patterns of expression that indicate their responsiveness to cytokine
treatment, particularly miR-10b-5p, miR-215 and miR-92a-3p. Several microRNAs were
differentially expressed at 36 and 25 hours. Further discussion of sequencing data and
corresponding computational analysis is discussed in chapter 4.
Quantitative PCR of individual microRNAs makes it evident that there are
differences in results arising from using a different platform for measuring expression. The
expression of five microRNAs that are differentially expressed at 25 hours namely miR1246, let-7c, miR-29a-3p, miR-210 and miR-21-3p, was validated with quantitative PCR.
Mir-19a, which is differentially expressed at 36 hours was also validate. These results
indicate that changes in expression of these microRNAs are reproducible across platforms.
63
)
In general terms, global microRNA expression data provided useful information
about the responsiveness of HCC cells to IFNy and TRAIL treatment that is instrumental for
a systems biology approach to study cell death and microRNAs.
f
64
)
CHAPTER 4
4.
Computational analysis of microRNA expression and cell death
4.1
Introduction
Cue-signal-response
approaches
have
been
used
successfully
to
develop
comprehensive mathematical models that shed light on different cellular pathways and
how they work to elicit different cellular responses [70-72].
These approaches often
incorporate heterogeneous arrays of information at the protein, RNA and phenotype level.
In order to include data from different experiments into a single model it is necessary to
first validate and normalize all the data sets. Once this is accomplished, a data driven
approach to model this cue-signal-response compendium, such as principal component
analysis (PCA) and partial least square regression (PLSR), is used to organize highly
dimensional collections of signaling measurements, to predict a cellular response from
multiple signals, and to select the most predictive markers for specified outcomes. These
approaches are applied to global microRNA expression data. With the use of high
throughput sequencing, it is possible to obtain expression data from all known human
microRNAs. This data, being highly dimensional, is well suited for PCA and PLSR analyses.
With this in mind, we set out to use a number of data-driven computational biology
techniques to predict cell death from microRNA expression and find which microRNAs are
relevant in cell death decision processes. We use PCA to categorize different cytokine
treatments on Huh7 cells based on microRNA expression. PLSR was used to predict cell
death form microRNA expression. DESeq was used to find differentially expressed
microRNAs across different cytokine treatment conditions and time points.
4.2
Experimental Procedures
4.2.1 Multivariate analysis of microRNA expression data using principal component
analysis (PCA)
Small RNAs were isolated from 93 different samples, corresponding to three sets of
biological replicates from each of the 31 conditions listed in Table 3. Each set of treatment
65
conditions was collected on different dates. Of those 93 samples, 20 were discarded
because quality control criteria were not attained. MicroRNAs with a maximum expression
level of less than 100 reads per million were removed. The resulting data matrix contains
information on 168 microRNAs for 73 samples representing 30 cytokine conditions. Out of
those microRNAs, 98 are listed in the high-confidence list in miRBase [41]
Prior to analysis, each microRNA expression column in the final expression data
matrix was centered by subtracting its mean and then scaled by dividing by its standard
deviation using the zscore function in MATLAB. PCA was completed using the princomp
function in MATLAB. This analysis method creates super-axes, called principal components
that incorporate information from expression of multiple microRNAs to reduce the
dimensionality of the data and minimize the noise from spurious fluctuations. The principal
components are created by decomposing the data matrix into a matrix of column vectors
called scores and row vectors called loadings. In this case, the scores correspond to
microRNAs and the loadings correspond to the different time dependent conditions.
4.2.2
Multivariate analysis of microRNA expression data using partial least square
regression (PLSR)
PLSR was used to predict cell death as a function of microRNA expression. The data
matrix described previously, and used for PCA, was rearranged to accommodate PLSR
requirements and to predict cell death from microRNA expression. Along with microRNA
expression, this matrix contains cell death information, as measured by LDH activity at 72
hours, for 6 different cytokine treatments. Each of these treatments was completed for 3
biological replicates, resulting in a cell death vector with 18 entries. The structure of the
part of the matrix containing microRNA expression data was arranged so that for a
particular treatment, all microRNAs expressed across the 8 different time points are placed
in the same row. The time at which each microRNA is expressed is explicit to evaluate time
related dependencies of the model. The experiment completed to generate all the data is
described in Figure 3.
(
66
J
PLSR identifies a linear solution to define the relationship between microRNA
expression and cell death. PLSR uses the SIMPLS algorithm, first centering X and Y by
subtracting off column means to get centered variables Xo and Yo. However, this method
does not rescale the columns. To perform PLS with standardized variables, we first use
zscore to normalize X and Y by mean centering and variance scaling.
The predictive power of the microRNA-cell death model was calculated by leaveone-out cross-validation and by using cross-validation coefficient,
Q2, and
the slope of the
predicted vs. observed plot. Separate models were built for different subsets of
differentially expressed microRNAs and different sets of time-points.
4.2.3 Model reduction by treatment time and microRNA relevance
criteria
The effectiveness of data-driven models, such as PLSR, can suffer from having
information that is irrelevant or noisy. It is therefore very important to select features of
the models that correctly predict a response and have biological relevance. Model
reduction approaches performed on PLSR analysis are explained here.
Expression of
microRNAs at each time point was regressed against cell death for each of the 6 different
cytokine conditions, using Pearson correlation. The resulting Pearson correlation
coefficient, R, and p values, were used as filter to reduce model size. MicroRNAs with R
greater or equal than 0.5 and a p value of less than 0.05 were kept and included in the
model.
4.2.3.1 Model reduction by time points and microRNA correlation to cell death
Several PLSR models were constructed using different time-course subsets, and
microRNAs that are correlated to cell death at different time points. The expression profiles
were divided between the early (0-24 hours) to late (25-48 hours) time points to identify
what time-dependent expression pattern shows greater model fitness and prediction
power.
67
4.2.3.2 Model reduction by variable importance of projection (VIP) scores
The information content of each microRNA for the PLSR model was assessed by its
variable importance of projection (VIP) score [70, 84, 85] to identify the relative
importance of individual microRNAs at each time point using the following equation:
1
Ka=1wa,kSa
VIPk= KXA wk
a=1SSa
where K is the total number of signaling metrics,
wa,k is
the weight of the k-th metric for
principal component a, A is the total number of principal components, and SSa is the sum of
squares explained by principal component a. Signaling metrics with a VIP > 1 have
significant importance in the model and metrics with a VIP << 1 significantly lack unique
information in the model. This analysis was performed to reduce the initial 168 microRNA
model, and find predictive relevance among those microRNAs
4.2.3.3 Model reduction by microRNA abundance and high confidence status
As described in section 3.3.3.1, microRNAs were ranked according to their average
abundance and their abundance at 25 and 36 hours. The top ten abundant microRNAs are
listed in Table 6. A PLSR model containing only those microRNAs was executed and
evaluated for its predictive power.
4.2.3.4 Model reduction by time points and differential expression analysis
Different models were created to investigate the importance of time of expression in
predicting cell death. First, several models were created using only the data for a particular
time point or sets of time points to investigate the importance of expression at different
time point. The predictive power and fitness of these models were evaluated by crossvalidation. Then, microRNAs that were differentially express across time points were
identified using the DESeq package for R, doing pairwise comparisons across conditions
and time-points. Table 7 includes the microRNAs that are differentially expressed at 36
and 25 hours, and were used to create reduced PLSR models.
f
68
4.2.4 Gene ontology analysis
The goal of this analysis is to generate a catalogue of genes expressed in human
Huh7 hepatocellular carcinoma cell lines and to identify potential target candidates for a
set of microRNAs up-regulated after 24 hours of IFNy treatment followed by 1 hour of
exposure to TRAIL, for a total of 25 hours of treatment. The overall strategy is based on the
definition of microRNA targets and the processing of microarray data to obtain gene
expression data. In this analysis, all known microRNA targets are used as background and
targets of microRNAs differentially expressed at 25, which are also reported to be
expressed in Huh7 cells in a number of data sets available from the gene expression
omnibus (GEO) are used as the foreground.
MicroRNA target tables were downloaded from the TargetScan website and
processed using custom scripts. Seeds of the microRNA of interests were used to query
multi-species transcript databases, and only sites with a Probability of Conserved Targeting
(PCT) score >0 were considered, as they have been shown experimentally to be more likely
to lead to transcript down regulation and decreased translational output by luciferase
assays [40]. List of genes at varying PCT levels (>0, >=0.3 and >=0.6) were prepared by
pooling all targets of all under consideration microRNAs, tallying the number of
independents microRNA seeds in their 3' UTR region to evaluate which gene might have
been under increased regulation during the course of the experiment.
Microarrays data were retrieved from GEO using references from Huh7 gene
expression studies. CEL files were processed in the R statistical package (R.3.0.2) using
several Bioconductor packages (affy, limma, gcrma and xps). Degradation plots were
generated using the AffyRNAdeg function and inspected, to check for the consistency in 3'/5'
biases among samples/replicates in a given data series. Images of the arrays themselves
were visualized to confirm the absence of blemishes. Data were normalized using gcrma
[86] and inter-sample Pearson correlations were recorded (>0.98). After normalization of
the data, presence/absence calls for expression were performed using the mas5calls
function. Genes deemed to be expressed in both replicates were identified for microRNA
target identification.
69
Candidate targets for the different microRNA lists were filtered against the
expressed genes obtained from the array data and imported into DAVID as gene expression
foreground. Backgrounds were defined in two ways: complete list of genes deemed to be
present/expressed in Huh7, to establish the functional enrichment of the specific targets of
these microRNAs, and also against the subset of expressed genes that bear any microRNA
seed at the same PCT threshold, to account for the fact that biases in the subset of
transcripts for which microRNA seeds have been identified may occur. Enrichments are
reported along with different multiple-comparison-corrected p-values (e.g. BenjamiHochberg).
4.3
Results
4.3.1 Multivariate analysis, using principal component analysis of microRNA
expression categorizes the conditions according to cell response
The PCA loadings plot (Figure 16) shows how different samples separate in
principal component space. This separation is based on the expression profile from all the
samples. Each microRNA is mapped to the different principal components, the samples are
defined as vectors called loadings in principal component space. As seen in Figure 16 the
different cytokine conditions were loosely, but clearly separated in 3 regions. The first
region, which we call the initial phase, is located in the second quadrant (Q II) and
corresponds to the early time points for all conditions (0-24 hours) before the second
treatment is applied. The second region, which we call the decision phase, is located in
third and fourth quadrants (Q III & IV) and corresponds to the conditions at the latter time
points for all the treatments except IFNy-TRAIL. The third region, which we call the death
phase, is located in the first quadrant (Q I) in principal component space, correspond to the
samples with the IFNy-TRAIL treatment at 36-72 hours and TRAIL-IFNy at 72 hours. Given
that the IFNy-TRAIL treatment is the condition in which the cells have a stronger death
response, it is possible to say that microRNA expression co-varies with cell death. This
means that microRNA expression profiles reflect duration of the cytokine treatments and
cellular death response.
70
)
0.5hrs
0.4 -
Region 3:
Death phase
Region 1:
Initial phase
0.3-
o
*
0
1hrrs
0.2
4
0.1
24hrs\
Region 2:
-0
*
o
0
*
N70
NT
IFNg
IRAIL
NT-IRAIL
7RAIL4FNg
IFNg-TRAIL
ZZ-7"
25hrs
Decision phase
36hrs
I48 hrs
.0 2
72 hrs
-0 3C
.0 4.
.0.
FN
I
0.4
I
.03
I
.0-1
-0.2
PC
0
________________1____
0.1
0.2
0.4
0.3
0.5
Figure 16. PCA analysis of microRNA expression for different cytokine treatments.
A. Treatments are categorized in principal component space in three regions: inithial phase, decision
phase and death phase. B. PCA model captures most of the variance in PC1 and 2.
4.3.2
Multivariate analysis using partial least square regression defines a function
to predict cell death from microRNA expression.
The relationship between microRNA expression and cell death was evaluated using
PLSR. As with PCA, PLSR separates the variables into a reduced number of principal
components. In contrast to PCA, the principal components of PLSR are orthogonalized to
the dependent variable, in this case cell death. The data structure for PLSR is slightly
different to PCA because it incorporates cell death as a variable dependent on microRNA
expression. There are 6 possible cytokine stimulation treatments, each producing a certain
level of cell death and measured for 3 biological replicates at 72 hours, resulting in a vector
of size 18 for the dependent variable. The microRNA expression matrix must then be of size
18 x n, where n is the number of microRNAs multiplied by the number of time points. This
is necessary in order to find a linear function to predict cell death. Expression data from the
72 hours time point was also removed. The conditions were separated according to their
-
-71
___
loading in principal component space, shown in Figure 17. The loadings for each cytokine
treatment correspond to the combined scores of all microRNA expression across all timepoints. Principal component 1 (PC1) separated the treatments according to the different
levels of cell death, while PC2 separated the no-treatment conditions from those that were
treated with a single cytokine, resulting in 4 treatment clusters.
With PLSR, microRNA expression can be used to define a linear function that
predicts cell death. Leave-one-out cross-validation determines how good the model fit is at
predicting cell death. A common and simple approach to evaluate models is to regress
predicted vs. observed values to evaluate fitness using the slope of this regression and the
correlation parameter
Q2. A perfect model would
have slope and
Q2 values
of exactly 1; the
closest these values are to 1, the better the model is at predicting an outcome. In this case,
the resulting PLSR model shows good fitness with a correlation parameter
Q2 =
slope of 0.66 (Figure 18).
0.2 -
-0
)(
.
0 15
-0 1J-
N
'U
oFT
_
V
C-0 15
-2
-1
0
1
PC 1
2
94% of response variance explained
41% of expression variance explained
Figure 17. PLSR model loadings plot in principal component space.
- -
72)--
3
4
0.69, and
4.3.3 Model reduction by treatment time and microRNA relevance
criteria
4.3.3.1 Model reduction by time points and microRNA correlation to cell death
Particular subsets of data were examined more closely to explore which time-points
were predictive of cell death. The full model incorporates all replicates at all time points.
Interestingly, the 25-48 hour data subset improved cell death prediction power. Another
way to reduce and improve the model is reducing the number of microRNAs included in the
model using Pearson correlation. MicroRNA expression at each time point was regressed
against cell death for the 6 different cytokine conditions. The resulting Pearson correlation
coefficient, R, and p values, were used as filter to reduce model size. MicroRNAs with R
greater or equal than 0.5 and a p value of less than 0.05 were kept and included in the
model. Table 8 shows the microRNAs more highly correlated with cell death. Table 9 shows
the cross-validation parameters resulting from time-reduced models.
Table 8. MicroRNAs highly correlated to cell death.
microRNA
time point
(hours)
R
(Pearson correlation
coefficient)
miR-193a-5p
miR-375
miR-375
miR-146b-5p
miR-146b-5p
miR-193a-5p
miR-92b-3p
miR-92b-3p
miR-196b-5p
miR-92b-3p
miR-23b-3p
miR-196b-5p
miR-19a-3p
miR-19b-3p
miR-19a-3p
miR-19b-3p
miR-23b-3p
36
36
48
25
36
25
48
36
48
25
36
36
48
36
36
48
48
-0.832535765
-0.752695261
-0.741331843
-0.595849837
-0.573782058
-0.54850013
-0.507906057
-0.505344207
0.608172245
0.622774198
0.671679661
0.687819315
0.732515163
0.739498196
0.748142342
0.759752351
0.835777315
(
73
Pearson's p value
1.81275E-05
0.000312622
0.000430526
0.009068841
0.012779871
0.018422271
0.031404142
0.032413523
0.007409174
0.005769479
0.002268223
0.001605406
0.000545907
0.000452661
0.000356089
0.000254104
1.5672E-05
Model reduced by
correlation coefficient
Full expression
data set
A
0-48
30
30
25
25
20
15
hours
15
10
0
-5
y=0.69x + 2.7
-5 - +
i
_2
-10 0
5
10
15
20
25
30 -
25-48
hours
y
-5
30
0
5
20
20
15-
15
10
10
C
0**.
0
-
S
10
--
15
-e
-
-
=
0.88
20
20
25
25
30
D
0.90X +.0
1.0
y=0.89x +
--
15
Q2
25
-10
10
30
+
25
-5
-= 0.74x + 2.5
-10
.
-- 30
30
-10
- 0
5
10
15
20
25
30
Observed cell death
(LDH activity %)
Figure 18: Evaluation of PLSR models with different subsets of expression data.
Separate models were built for different subset of time periods, including the full expression matrix
(A), and for 25 to 48 hours (C). Reduced models (B,D) were constructed for microRNA expression with
high Pearson correlation coefficient (IRI < 0.5) and low p values (p < 0.05)
Table 9: Model evaluation for several PLSR models reduced by time course.
Data included
All microRNAs
time points
slope
Q2
0-24 hours
-lx10"
7.6x10_2
25 hours
0.57
0.42
36 hours
-1x101
6x10-6
48 hours
-9x10 13
1.2x10 3
An important result discovered in this study is that, although there are more
microRNA expression changes that occur at 36 hours, expression data from the 25 hour
time point alone has small predictive power, while all other time points do not result in
models that have any predictive power. With a cross-validation slope of 0.57 and a
Q2 of
0.42, this model is far from perfect, but there is definitely some a correlation between
expression changes at 25 hours and cell death.
(
74
)
4.3.3.2 Model reduction by variable importance of projection (VIP) scores
A way to remove irrelevant information from PLSR models is by assigning VIP
scores to the independent variables included in the models. VIP scores are calculated from
the weight of each expression point onto a principal component relative to the number of
expression points and the variance explained by that principal component. MicroRNAs are
ranked by their VIP scores and grouped in quartiles, by evaluating the cumulative
distribution of VIP scores in the population (Figure 19). Each quartile includes 25 % of the
cumulative weight of VIP scores. Quartiles were removed, starting with the lowest scoring
microRNAs (1st quartile) until only the higher scoring microRNAs remain
(4th
quartile, VIP
> 1.23, 710 expression points).
Table 10 includes information on the cross validation parameter
Q2
and how
reducing the model by VIP scores affects model performance. Models including microRNAs
with increasing VIP scores show marginal improvement in their predictive power for the
full time course model.
3
2.75
2.5
2.25
2
1.75
VIP 1.5
1.25
.
0.75
0.25
0
Figure 19. Cumulative distribution of VIP scores for microRNAs.
r 75
Table 10. Model reduction by VIP scores and time points and the effect on model performance
parameters.
Time points VIP quartiles
included
removed
Q2
0-48 hours
0
0.7724
0-48 hours
1
0.7854
0-48 hours
2
0.7782
0-48 hours
3
0.7710
25-48 hours
0
0.8298
25-48 hours
1
0.8344
25-48 hours
2
0.8401
25-48 hours
3
0.8166
Figure 20 shows the top 20 scoring microRNAs, ranked by VIP scores. Out of those,
miR-1246, miR-615-3p, and miR-92b-3p are also differentially expressed at 25 hours. Not
included in this list is miR-21-3p, which ranks at 24, in terms of VIP score ad is also
differentially expressed at 25 hours.
VIP scores
miR-4461-25
mIR-664-3p-48
mIR-3195-72
mIR-615-3p-72
miR-27a-3p-72
mIR-671-3p-36
miR-3687-48
mIR-215-36
miR-106b-3p-72
mIR-92b-3p-48
mIR-221-3p-72
miR-25-5p-25
miR-1307-Sp-72
mIR-192-5p-72
mIR-4488-72
mIR-4449-72
mIR-378a-Sp-48
miR-1246-72
mIR-2467-Sp-72
miR-4485-72
hr
hr
hr
hr
hr
hr
hr
hr
hr
hr
hr
hr
hr
hr
hr
hr
hr
hr
hr
hr
0
0
0.5
0.51
1.52
1
1.s
Figure 20. Top 20 microRNAs ranked by VIP scores.
I
76
2-
2
2.5
4.3.3.3 Model reduction by microRNA abundance and high confidence status
The model was reduced to include only those microRNAs with high-confidence
status listed by miRBase. These microRNAs are more likely to be functional and to target
mRNAs, however, this list is evolving and is likely change in the near future. A few relevant
microRNAs are not listed as high- relevance, including miR-122, miR-221, miR-222, let-7c,
miR-1246 and miR-5701. The first four microRNAs listed previously are validated and have
known targets. The high-confidence miRNA list is an evolving endeavor by the Sanger
Institute and will probably improve with time.
30,
25-
20-
15
10-
5S
0
.
-5-0
0
5
10
15
Obsenred LDH %
20
Adjusted data
Fit: y=0.658047*x
95% conf. bounds
25
30
Figure 21. Model including high confidence microRNA list
Out of 168 microRNAs included in the PCA and PLSR models, 98 met the criteria for
high-confidence (listed in Appendix B, Table B. 2). The resulting model has a slope of 0.658
and a
Q2 of 0.575.
This model reduced predictive power and performance, as comapare to
the full expression matrix model with a slope of 0.66 and
Q2 of 0.69.
Another way to look at biological relevance of the microRNAs included in the
models is to rank microRNAs according to their relative abundance in the population of
expressed microRNAs across samples. The reduced model included 10 microRNAs that are
77---
--
the most abundantly expressed at 25 and 36 hours. These 10 microRNAs also happen to be,
on average, the most abundantly expressed microRNAs across all samples.
30
F-
25
20
0
15
I
10~
.
5
0
0
-5
--
g
-10
0
5
-10
15
Obserned LDH %
Adjusted data
Fit: y=0.725576*x
95% conf. bounds
r
20
r
25
30
Figure 22. Model performance by reduction to top 10 abundant microRNAs
This reduced model has a slope of 0.726 and a Q2 of 0.627. Although this model does
not perform particularly poorly, it does not significantly add predictive values over VIP
scores, or correlation reduced models.
4.3.3.4 Model reduction by differentially expressed microRNAs
Using the information from PCA and PLSR, we determined that the most relevant
changes for predicting cell death occur at 25 and 36 hours after the initial treatment. We
reduced the PLSR model by selecting only the microRNAs that are differentially expressed
at those time points.
Figure 23 shows models reduced by differentially expressed microRNAs at 25 and
36 hours.
-
78
Jv-
MicroRNAs
included in
reduced
Number of
microRNAs
Q2 , Percentile
Predicted vs observed LDH % for
Model evaluation
Histograms
Rank
model
40
011
35 -Fit
3
9 5% conf, bounds
Slope = 0.835
bow
Differentially
0.sA
2
expressed at
25and36
hours
,0.658,83.5%
-
5
10
1S
20
25
30
35
0
40
010.2
03
04
Observed LDH %
Differentially
expressed at
0.6
07
0
0
1
71AWa
40
I
05
QZ
-Fit
io 95% conif bounds
Slope =0.834
Q2=0.63
000
I
42
.
36 hours
,3D
0.628, 69.6%
00
-
10s
0 5
11) is 21) 25
Observed LDH %
i)30
S
0
44)
0,2 0.3 04 0,5 0.6 07 08 01) 1
Q2
40
.0
- Fit
95% conf. bounds
Slope
0 =0.846
Q
82
3s
a
25
~20
Differentially
expressed at
0Af
000
I
S
0879.
0.817,98.8%
-0-
12
25 hours
000
10
15
O 2S
Observed LDH %
30
Y,
4
0.0
03
04 0
.5
W60,7
0
0,
1
Q2
-0Fit
95% coot bounds00
Slope = 0.853
Differentially
expressed at
25 hours,
01
0
7000
Q 0.47
0.472,65.4%
5
PCR
20
validated
1
10
-20
1
0
1
20
30
40
0
Observed LDH %
0A
0.
0Z 0.3D
0.5
006
07
00
()0q
1
Q2
Figure 23. Model reduction by differentially expressed microRNAs
Reduced PLSR models were produced using only microRNAs that are differentially expressed after 25
and 36 hours of treatment, or validated by PCR.
These models include the full course of treatment but only include those microRNAs
that were differentially expressed at select time points. A histogram of the crosscorrelation parameter,
Q2,
of models with the same number of microRNAs selected at
random for 100,000 iterations is provided for comparison. The percentile rank
corresponds to the percentage of random models that did more poorly than the model with
_______-
----
A
79
-
the differentially expressed microRNA. Most models performed relatively well (ranked at <
60% of random models of the same size), but the 12 microRNA model with the microRNAs
that were differentially expressed at 25 hours was exceptionally good (ranked better than
98.8 % of random models).
By generating models with random lists of microRNAs, it is possible to examine the
content of the models that perform at a high level of predictive power. Interestingly, all of
the microRNAs that are differentially expressed at 25 hours are represented in these
models, although not as an inclusive set, but rather accompanied by other microRNAs.
4.3.4 Gene ontology
Results from gene ontology analysis include a diverse number of GO terms enriched for
targets of microRNAs that are differentially expressed at 25 hours after the initial
treatment (Table 11). A few interesting terms were enriched in this subset. Not
surprisingly, phosphoproteins were the highest ranked termed, in terms of protein that are
targeted by the 12 differentially expressed microRNAs. In general, terms related to cell
signaling and transcriptional regulation were found to be enriched within miRNA targets.
Further assessment is being done to examine the expression of a few of the targets of the
differentially expressed microRNAs.
f
80
]
Table 11. Gene ontology terms enriched in targets of 12 differentially expressed microRNAs at 25
hours post-initial treatment.
Term
%
PValue
Total
Enrichment
Bonferroni
Benjamini
FDR
Phosphoprotein
63.51
1.93E-09
1013
1.15E+00
9.59E-07
9.59E-07
2.78E-06
Alternative splicing
48.32
9.82E-06
1013
1.15E+00
4.87E-03
1.22E-03
1.41E-02
Regulation of transcription
21.40
7.97E-07
840
1.33E+00
2.36E-03
2.36E-03
1.43E-03
Transcription regulation
16.96
1.72E-07
1013
1.43E+00
8.53E-05
4.26E-05
2.47E-04
Activator
5.92
1.22E-04
1013
1.62E+00
5.88E-02
8.62E-03
1.75E-01
Positive regulation of RNA metabolic
process
5.82
2.04E-05
840
1.71E+00
5.86E-02
1.00E-02
3.66E-02
Positive regulation of transcription,
DNA-dependent
5.72
2.70 E-05
840
1.71E+00
7.70E-02
1.14E-02
4.86E-02
Positive regulation of transcription from
RNA polymerase II promoter
4.64
7.11E-06
840
1.92E+00
2.09E-02
7.01E-03
1.28E-02
Pathways in cancer
4.04
1.70E-04
334
1.77E+00
2.55E-02
1.28E-02
2.03E-01
Regulation ofsmall GTPase mediated
signal transduction
3.45
2.54E-06
840
2.27E+00
7.50E-03
3.76E-03
4.57E-03
Regulation of Ras protein signal
transduction
2.96
1.60E-05
840
2.27E+00
4.64E-02
1.18E-02
2.88E-02
Focal adhesion
2.86
4.13E-05
334
2.19E+00
6.25E-03
6.25E-03
4.94E-02
Muscle tissue development
2.17
1.84E-05
840
2.67E+00
5.31E-02
1.09E-02
3.31E-02
Melanoma
1.38
2.58E-04
334
3.OOE+00
3.85E-02
1.30E-02
3.09E-01
4.4. Discussion
When used in combination, the cytokines IFNy and TRAIL induce cell death in Huh7
cells. These cytokines also have an effect on microRNA expression. The linear modeling
techniques PCA and PLSR are well-suited to handle the multivariate nature of microRNA
expression data from massively parallel sequencing.
PCA was useful for establishing a correlation between the different conditions and
microRNA expression. Additionally, this analysis revealed that treatment duration
correlates with microRNA expression in principal component space. Even though PCA does
not take into account cellular death response information, the difference in microRNA
expression profiles of the conditions with the strongest cell death response was enough to
identify a marked difference. These results indicate that there is a correlation between
microRNA expression and cell death.
f
81
PLSR was successful in defining a function to predict cell death from microRNA
expression. Several approaches were used to reduce the model and inspect model
performance, to select features that are informative of the system workings. After further
inspecting subsets of data, based on different durations of treatment, it was clear that part
of the data accounted for most of the predictive power of the model.
The full data sets with all microRNA and time points did a reasonable job at
predicting cell death. However, by examining models with different subsets of data, model
performance was improved, highly predictive microRNAs were found, and a single time
point, namely 25 hours (1 hour after TRAIL treatment on IFNy sensitized cells) was found
to be particularly well-suited for capturing the variance in the data and predicting cell
death. Another interesting time point is 36 hours (12 hour after TRAIL treatment on IFNy
sensitized cells), at which a larger number of microRNAs are differentially expressed.
Model performance was improved by including 12 microRNAs that were
differentially expressed at 25 hours. The model constructed by using the time
course expression of these 12 microRNAs was found to do better than 98 % of other
models with the same number of microRNAs (
Figure 23). When comparing the performance of the model including these 12 microRNAs
with other reduced models, it is apparent that they are good at predicting cell death.
Other approaches to reduce PLSR models of cell death as a function of microRNA
expression included reduction by microRNA abundance, high-confidence status and VIP
scores. Using only the high confidence set actually reduced model predictive power. The
model reducing by abundance of microRNAs, which only includes 10 microRNAs
performed at a level similar to the full model. Reducing the model by VIP scores and
relevant time frames improved model performance. However, it is hard to link these results
with biological significance of VIP values and the microRNAs that rank high on this list.
Gene ontology analysis gave us a window into what plausible pathways are being
affected through microRNAs in this system. Additional work is under way to determine the
relative expression of a few targets of the differentially expressed microRNAs. These
analyses will give us more inside into the role of these microRNAs in cell death pathways.
f
82
)
CHAPTER 5
5. Conclusions and future direction
5.1
Emergence of systems biology analyses for microRNA expression in the
context of cell decisions and hepatocellular carcinoma
MicroRNAs are considered to be a fine-tuning device of the cell; in most cases they
do not completely obliterate expression of their target, but rather minimize the expression
of multiple proteins, resulting in a system wide effect that can affect cell phenotypic
behavior [12, 60]. In some cases, they can also act as a switch and induce high levels of
repression on their target mRNAs [81]. In the field of microRNA research, a number of
studies have focused on phenotypic changes brought upon by external perturbations and
changes in microRNA expression that correlates with either the phenotypic changes, or the
external perturbation. A few studies pertain to microRNA expression dynamics upon
treatment in systems similar to the one studies here [32, 33, 63, 64]. Interestingly, up until
now, there have not been studies that incorporate correlations between an external
perturbation to the system, the resulting microRNA expression changes along with changes
in cell phenotypic responses and the time frames at which all of these processes are
orchestrated. To achieve this, the holistic nature of a systems biology approach is necessary
to achieve a higher level of integration between multiple components of the system.
The studies presented here shed light not only about the identity of microRNAs that
can predict cell death, but also on cytokines that can bring about relevant change in cell
behavior, the time frames at which the microRNA become relevant and ways in which we
can investigate this questions in depth. These studies are also relevant for finding new
ways to study, diagnose, and treat cancer. In this study, the focus is on hepatocellular
carcinoma, however this approach could be applied to any system in which cell death and
microRNAs are relevant. Uncovering the identity of microRNAs that are predictive of cell
death gets us closer to using these biological entities for impactful medical uses. Learning
about the time frame at which expression changes matter can guide future primary
[
83
research endeavor to uncover even more relevant biological entities with constantly
changing nature that is part of their functionality.
The purpose of this research was to link a pro-apoptotic cytokine treatment to
microRNA expression and cell death and to incorporate information on time dependencies
of those links. The context for this study was a hepatocellular carcinoma cell line model.
Although, limited in scope, the results obtained here can aid in the development of better
research studies on microRNAs and improve on medical uses of these molecules.
5.2
Cytokine induced cell death in hepatocytes
After evaluating two different HCC cell lines, Huh7 and Hep3B, and their response to
different cell death receptor ligands, FasL, TNFa and TRAIL, it was demonstrated that
TRAIL induced higher levels of apoptosis and, furthermore, that Huh7 cells are more
susceptible to the apoptotic effects of TRAIL than Hep3B cells. With these studies, it was
determined that Huh7 cells, treated with 10 ng/mL IFNy followed by 100 ng/mL TRAIL
will produce the maximum amount of cell death in this system, when measured at 48 and
72 hours after the initial IFNy treatment. The LDH cytotoxicity assay was used to measure
cell death so that microRNA expression could be measured concomitantly from the same
samples. These studies established a model system to study microRNA expression changes
and cell death. Single cytokine treatments with either TRAIL or IFNy failed to induce
apoptosis, and were used for comparing cell death results and microRNA expression in
subsequent analyses.
5.3
Measurements of microRNAs expression changes induced by IFNy and
TRAIL in HCC cell lines
Measuring microRNA expression is challenging for many reasons. First, there is little
information about which microRNAs play an integral a role in the system of interest. Using
global profiling proved to be the most strategic approach that permitted the examination of
all annotated microRNAs at multiple time points, therefore it allowed for more freedom in
investigating a large data set and discovering relevant microRNAs. Second, there are
84
J
problems with inter-platform reproducibility. From the various platforms, high throughput
sequencing is the best option to examine microRNA expression quantitatively because: it
provides a direct read of microRNA sequences, so all microRNAs are read and quantitated
at once from each sample; it does not require high amounts of total RNA for good
performance, but does require that RNA to be high in quality; and it allows for pooling
multiple samples together into the same flow cell.
Out of almost two thousand microRNAs annotated in miRBase, 168 were detected
across all the samples submitted for sequencing. Across all conditions, microRNA
expression changes were markedly different in the IFNy-TRAIL condition, 36 hours after
the initial IFNy treatment. After having performed further computational analysis,
microRNA expression at the 25 hour time point was examined in more detail as well.
MicroRNAs that were differentially expressed at 25 hours followed different
expression patterns and have interesting protein targets that are likely to play a role in
orchestrating cell death.
5.4
Computational Analysis of microRNA expression and cell death
MicroRNA expression data from Illumina sequencing was subjected to two
multivariate analysis methods: PCA and PLSR. PCA loadings plot exhibited the time
progression of microRNA expression and correctly separated different cytokine treatment
conditions according cell death status. It also revealed that there were marked differences
in expression between cytokine treatments at 36 hours after the initial treatment.
PLSR models proved that microRNA expression is a good predictor of cell death. The
cross-validation parameter
Q2 was
used to evaluate model fitness and predictive power.
PLSR models were reduced removing data from particular time points and/or including
only microRNAs that were differentially expressed at relevant time points. The model was
optimized by using data from time points later than 24 hours and it was further improved
by including the full time course of only those microRNAs that were differentially
expressed 25 hours after the initial treatment. This emphasizes the fact that these 12
microRNAs could be some of the best predictors of cell death. These microRNAs are likely
f
85
to be playing an active role in cell death pathways, and so their targets might be
informative of how they fit to fine tune protein signaling networks. Gene ontology analysis
of the targets of these 12 differentially expressed microRNAs was performed to find which
terms are enriched. To narrow down the number of targets used for gene ontology analysis,
one of the criteria was to only select genes than have been reported to be expressed in
Huh7 cell lines by other groups. Terms related to cell signaling and regulation of
transcription were found to be enriched.
Interestingly, some individual targets of these microRNAs are directly involved in
cell survival pathways active in the context of HCC (Figure 24). For instance, PTEN is
targeted by miR-92b, miR-29a and miR-181a, which are all highly predictive of cell death.
There are other examples of this in Figure 24.
TNF -a
TRAILl
FasL
RTK
L8-6
receptor
T
AT
Caspase 8,
ATR92
10
miR-21
ri-9
miR-29aa
let-7c
t
Apptosofn
translation,
transcription
mniRNAs
Figure 24. Survival and apoptosis pathways in HCC and possible targets of relevant microRNAs
86
5.5
Future directions
This project answered a few questions about the temporal dynamics of microRNA
expression and their predictive power for cell decision processes. It will be very interesting
to explore additional avenues of research that are directly related to these questions. One
possible expansion of this research could be to systematically include cellular signaling
networks create a more comprehensive model. To do that, we could measure expression
levels of target of highly predictive microRNAs and incorporate that to the model. The
challenging aspect of this possible avenue of investigation is the quantitative corellations
between mRNA, microRNA and protein expression are rarely simple. It might require a
different mathematical methodology to incorporate data from such diverse sources.
Another relevant and interesting question to answer is whether these microRNAs
are also active players in cell death decision by perturbing the microRNA landscape. To do
that, we would have to introduce select microRNA inhibitors and mimics and measure cell
response as an output of microRNA perturbation. This questions was partially explores for
miR-122 and miR-192. The results are presented in Appendix A. These types of studies
could be complicated by having to construct inhibitors, mimics and reporters the
microRNA examined. Unlike expression profiling of microRNAs, these types of studies
cannot be done for a large number of microRNAs. To successfully investigate microRNA
perturbations, it is necessary to first know more about the role of microRNAs in the system
with an approach similar to the one in which the work presented in this thesis is focused.
(87
APPENDICES
Appendix A: Perturbation of microRNA activity by mimics and sponges
A.1 Introduction
MicroRNA activity can be perturbed by competitive inhibition and an increase in
microRNA abundance. Inhibition of microRNA activity was achieved using sponges, which
were obtained from the Phillip Sharp's lab at MIT. These competitive inhibitors of
microRNAs are transcripts encoded under strong promoters that can be expressed in cells
as RNAs produced from transgenes. When vectors encoding these sponges are transiently
transfected into cultured cells, sponges depress microRNA targets. MicroRNA activity was
increased by transfecting cells with mimics, which are small oligonucleotides synthesized
to be identical to the mature form of the microRNA. Mimics were purchased from
Dharmacon.
A.2 Experimental procedures
Figure A.1 depict the experimental approach to test the effects of sponges and
mimics.
Day 2: Change media, Transfect
Day 3: 1st treatments
(IFNy /TRAIL)
Day 1:Plate cells in
media with FBS at
3x104 cell /cm 2
t_-o
I
24 hrs
Day 4: 2nd treatments
(TRAIL)
t=72
t=48
t=24
24 hrs
24 hrs
Day 5: Finish
experiments
24 hrs
Figure A. 1. Experimental approach to study microRNA perturbations with sponges and mimics.
Prior to transfection of cells, sponges and Renilla luciferase reporter genes were
constructed by designing a microRNA target sites for the species of interest. These targets
-- (
88
were design as 8 tandemly arrayed microRNA binding sites. For Renilla luciferase, these
sites were placed into the 3' UTR of the gene, so it could be used as a reporter of microRNA
activity. These binding sites were perfectly complementary to the seed region of the
microRNA, with a bulge at positions 9-12 to prevent RNA interference-type cleavage and
degradation of the sponge RNA. Out of several attempts at constructing sponges and
reporter genes for several microRNAs of interest, miR-122 and miR-192 were successfully
made.
Huh7 cells were transfected using FuGene for sponges and Dharmafect duo for
mimics. The reporter genes from Renilla luciferase and a control gene from Firefly
luciferase were used to indicate microRNA activity, and were transfected with the sponges
and mimics. A day after transfection, these cells were treated with 10 ng/mL of IFNy. These
cells were treated with 100 ng/mL of TRAIL 24 hours after treatment with IFNy. The
cytotoxicity assay to measure the activity of LDH released from the liver cells into the
media when the cells die and break their membrane is used to measure cell death upon
transfection and cytokine treatment.
A.2 Results
As reported by the relative activity of Renilla luciferase and depicted in Figure A. 2,
microRNA mimics and sponges were effective in increasing and decreasing (respectively)
the activity of miR-122 and miR-192.
E sponge ctrl
miR-122 sponge
D mimic ctri
U
6
s
relative
Renilla
luciferase
activity
E
5
miR-122 mimic
10
*
sponge ctrl
* miR-192 sponge
0 mimic ctri
M miR-192 mimic
9
8
7
6
4
5
3
43o
(norrnlzetnotrNx
notrtm)
3
2
0
IFNg
TRAIL
0
+
+
+
+
+
c>+
b
g
Figure A. 2. MicroRNA activity, as reported by dual luciferase assay is e fectively changed by sponges and mimics
for miR-122 and miR-192
89
The stars and brackets above the bars indicate statistical significance of activity
difference, as indicated by a t-test p-value lower than 0.05.
The effect of microRNA activity on cell death was evaluated by transfecting cells
with sponges and mimics, along with the reporter luciferase genes and treating with IFNy
at 10 ng/mL for 24 hours, followed by TRAIL at a concentration of 100 ng/mL, for another
24 hours. Figure A. 3 illustrates results from LDH activity. No strong evidence was found to
indicate a correlation between the perturbed activity of these two microRNAs, miR-122
and miR-192 and cell death. These results could also be confounded by high levels of cell
death induced by transfection only in the cytokine combination condition.
35
0
*
30
"
no trfx
sponge ctrl
miR-122 sponge
E0 no trfx
6
0 mimic ctri
" miR-122 mimic
25
0 sponge ctrl
0 miR-192 sponge
Q mimic ctri
miR-192 mimic
7
5
20
LDH A
(norma
notrtm)
3
10
5
IFNg
TRAIL
[
--
-
I I
01
-
1+
+
_
+
-
Figi ire A. 3. Cell death, as measured by LDH activity, is not affected by a change in activity in miR-122
and miR-192.
A.2 Discussion
Sponges are effective in reducing the availability of the microRNA to bind to target
genes. Mimics increase microRNA levels and bind to target genes. However, there was not
enough evidence to prove a causal correlation between activity levels of miR-122and miR192 and cell death. These results are confounded by the fact that, in the presence of the
cytokine combination treatment, transfection induces a significant amount of cell death by
itself. In light of microRNA global measurements and multivariate analysis, it is possible
that these two microRNAs, not being part of the set of highly predictive species, do not play
a role in TRAIL induced cell death and cannot elicit a change in the cell death response.
{
90
1
Appendix B: Tables for evaluation of microRNAs for model reduction
based on relative abundance and high-confidence.
B.1 Relative microRNA expression data from a systematic approach to
cytokine treatment and cell death experiment measured by next generation
sequencing on the Illumina platform
The data matrix generated from the systematic cytokine treatment experiments
described in chapters 2-4 was too extensive to present in the thesis. It is still a relevant
piece of information. To provide some insights into the data, the table included here
contains the average relative microRNA expression, ranked by percentage of abundance.
This data was described in section 3.3.3.1. Percentage of abundance is calculated by
dividing the number of reads microRNAs on each sample by the total number of reads in
the sample. The abundance rank is based on the average percentage across samples.
Table B. 1. MicroRNA abundance and expression changes for the 168 microRNAs included in the multivariate
models.
% change at 36 hours
% change at 25 hours
Average
miRNA
miR-10b-5p
miR-192-5p
miR-215
miR-148a-3p
miR-21-5p
miR-22-3p
miR-191-Sp
miR-92a-3p
miR-122-5p
miR-27b-3p
miR-30d-5p
miR-26a-5p
miR-182-5p
miR-10a-5p
miR-194-5p
miR-30e-5p
miR-21-3p
miR-92b-3p
miR-181a-Sp
miR-378a-3p
miR-25-3p
miR-16-5p
miR-15la-3p
miR-186-5p
miR-28-3p
let-7a-5p
miR-151a-Sp
miR-103a-3p
miR-93-5p
miR-183-5p
Average
Average
Abundance representa expression
tioninthe percentat
rank
population 25 hours
22.50%
18.36%
1
18.81%
18.26%
2
7.87%
6.42%
3
5.92%
6.30%
4
7.00%
6.28%
5
5.93%
6
5.47%
3.27%
7
3.72%
4.67%
3.40%
8
2.38%
2.62%
9
1.93%
2.06%
10
1.80%
1.67%
11
1.63%
1.63%
12
1.58%
1.63%
13
1.09%
1.12%
14
1.07%
1.06%
15
0.97%
1.03%
16
1.35%
0.87%
17
0.87%
1.46%
18
0.81%
19
0.82%
0.79%
0.93%
20
0.78%
0.73%
21
0.69%
0.67%
22
0.75%
0.69%
23
0.67%
0.77%
24
0.66%
25
0.57%
0.40%
0.44%
26
0.39%
0.38%
27
0.32%
0.33%
28
0.30%
0.30%
29
0.25%
0.29%
30
NT, t = 0
hours
NT, t = 24
I, t = 24
-33.52%
6.23%
62.19%
16.18%
13.73%
-4.79%
-19.61%
56.02%
7.57%
17.12%
7.05%
11.99%
12.07%
-22.19%
14.90%
24.86%
82.90%
104.57%
-2.11%
30.38%
7.42%
-6.13%
18.16%
15.70%
21.73%
-22.44%
-7.60%
4.76%
0.41%
-18.98%
-31.25%
-2.19%
66.69%
0.32%
46.51%
12.40%
-19.74%
58.95%
-28.05%
15.19%
11.28%
-12.11%
10.76%
-8.74%
3.33%
29.60%
164.02%
72.31%
-2.57%
34.62%
17.89%
-8.02%
15.28%
26.78%
43.98%
-35.54%
-13.37%
0.71%
-10.08%
-8.41%
-25.99%
-4.84%
47.32%
16.14%
2.21%
10.78%
-23.00%
59.73%
-3.36%
-2.87%
26.49%
3.28%
25.07%
-12.29%
-4.40%
14.01%
16.75%
41.43%
7.22%
25.40%
14.76%
1.58%
18.30%
12.62%
45.46%
-22.76%
-3.31%
20.30%
7.36%
-0.55%
91
expression
percentat
36 hours
20.98%
18.08%
6.05%
5.68%
8.99%
4.84%
2.84%
4.13%
1.84%
2.44%
1.57%
1.49%
1.74%
0.79%
1.00%
0.99%
1.48%
0.54%
0.84%
1.19%
1.10%
0.73%
0.74%
0.67%
0.49%
0.29%
0.21%
0.36%
0.30%
0.27%
NT, t = 0
hours
NT, t = 36
-24.02%
5.20%
24.82%
11.37%
46.06%
-22.40%
-30.22%
38.13%
-16.81%
38.97%
-6.46%
1.82%
23.69%
-44.92%
8.26%
20.28%
100.51%
-23.93%
1.84%
66.70%
52.28%
1.54%
15.47%
1.34%
-9.74%
-42.41%
-50.09%
18.49%
-1.48%
-12.53%
17.99%
-4.46%
-28.86%
-22.15%
80.77%
-13.32%
-25.71%
11.38%
-42.20%
18.92%
-19.31%
-22.42%
5.34%
-17.97%
-11.17%
-18.63%
158.54%
-48.89%
9.94%
66.99%
51.90%
-6.29%
6.86%
-7.59%
-16.11%
-39.66%
-58.09%
1.68%
-9.04%
-18.94%
1, t = 36
8.41%
-5.87%
-25.18%
-0.75%
37.02%
-10.96%
-12.38%
3.50%
-26.84%
13.36%
-1.21%
-12.32%
-0.04%
-31.90%
-22.83%
-1.61%
43.55%
-63.72%
0.14%
35.86%
61.55%
9.87%
4.31%
-10.90%
-18.36%
-25.44%
-44.27%
19.64%
10.25%
11.21%
%change at 25 hours
Average
% change at 36 hours
Average
Average
miRNA
Abundance representa expression
rank
tioninthe percentat
population 25 hours
NT, t =0
hours
NT, t= 24
I, t =24
expression
percentat
36 hours
NT, t =0
hours
NT, t = 36
miR-148b-3p
-40.22%
-15.98%
-34.65%
-48.62%
-11.44%
-42.99%
-11.84%
0.17%
0.16%
0.16%
11.59%
7.06%
49.50%
47.99%
6.25%
-38.91%
21.11%
-1.24%
12.43%
40.65%
9.22%
9.54%
-26.12%
35.03%
-24.94%
9.23%
24.81%
22.92%
42.37%
7.64%
-28.54%
11.45%
4.90%
21.29%
0.51%
6.87%
1.22%
13.07%
-36.51%
-36.71%
-41.59%
miR-769-5p
niR-148a-5p
-13.54%
-19.34%
45.62%
25.67%
-2.90%
-43.64%
2.16%
-19.81%
-4.73%
33.36%
12.88%
-1.97%
-19.14%
21.76%
1.67%
32.27%
8.92%
23.78%
43.68%
-10.06%
77.37%
3.04%
-1.02%
1.31%
-21.52%
2.82%
-13.42%
3.65%
-30.17%
-20.61%
-7.26%
miR-146a-5p
-15.29%
-5.95%
61.94%
41.63%
-21.57%
-58.43%
6.46%
10.97%
-3.64%
66.20%
11.60%
-0.83%
-34.96%
12.83%
128.85%
5.73%
-0.15%
12.99%
51.69%
1.41%
30.32%
4.36%
17.61%
10.05%
-11.08%
3.82%
-16.50%
-5.87%
0.20%
miR-181b-5p
0.27%
0.27%
0.25%
0.21%
0.19%
50.16%
-3.00%
-23.74%
2.21%
-47.25%
-30.75%
-30.68%
-40.42%
30.48%
165.52%
-13.69%
-35.26%
-6.72%
-71.41%
-18.71%
-27.95%
-31.94%
8.02%
207.25%
0.36%
-25.27%
-0.77%
-71.53%
50763.82%
33.68%
29.12%
32833.73%
15.15%
14.96%
7.78%
0.13%
23.00%
110.48%
100.80%
-10.67%
-11.37%
-7.10%
0.63%
-6.64%
-0.82%
4.94%
93.37%
-30.30%
-0.85%
-3.04%
-6.22%
-19.21%
10.84%
-0.82%
60.81%
-19.04%
-19.76%
51.49%
87.48%
10.34%
-8.21%
17.91%
9.52%
2.18%
-8.38%
3.32%
17.01%
134.36%
-40.69%
0.30%
-1.35%
-8.73%
-13.77%
6.38%
25.42%
47.04%
-14.54%
-12.46%
53.13%
85.88%
9.07%
-1.03%
0.47%
-1.85%
36.14%
-1.87%
-4.98%
-2.60%
38.95%
-40.00%
8.64%
3.08%
-4.78%
5.52%
-6.75%
4.71%
56.13%
-0.30%
-4.13%
22.62%
miR-26b-5p
miR-423-3p
miR-30c-5p
miR-320a
0.15%
miR-423-5p
0.12%
0.11%
miR-1304-3p
miR-20a-5p
rniR-126-5p
miR-140-3p
miR-4792
nmR-101-3p
miR-30b-Sp
miR-221-3p
miR-484
let-7f-5p
niR-1307-5p
miR-17-5p
niR-345-5p
miR-1269a
miR-196a-5p
miR-130b-3p
miR-589-5p
miR-941
miR-1269b
miR-128
miR-210
miR-425-5p
miR-561-5p
miR-27a-3p
niR-15b-5p
miR-30e-3p
miR-1303
miR-19b-3p
miR-29a-3p
miR-146b-5p
miR-106b-5p
miR-1307-3p
miR-28-5p
niR-30a-5p
miR-222-3p
miR-301a-3p
miR-1
97 3
- p
miR-421
miR-24-3p
miR-106b-3p
0.11%
0.10%
0.10%
0.09%
0.09%
0.09%
0.09%
0.09%
0.08%
0.08%
0.08%
0.07%
0.07%
0.07%
0.07%
0.06%
0.06%
0.06%
0.06%
0.06%
0.06%
0.05%
0.05%
0.05%
0.05%
0.05%
0.05%
0.05%
0.05%
0.04%
0.04%
0.04%
0.04%
0.04%
0.04%
0.04%
0.04%
0.04%
0.03%
I
0.25%
0.25%
0.34%
0.25%
0.19%
0.07%
0.17%
0.16%
0.14%
0.16%
0.15%
0.13%
0.09%
0.10%
0.00%
0.09%
0.09%
0.09%
0.12%
0.08%
0.08%
0.09%
0.09%
0.07%
0.06%
0.07%
0.07%
0.07%
0.06%
0.08%
0.06%
0.05%
0.06%
0.05%
0.05%
0.05%
0.06%
0.04%
0.07%
0.04%
0.05%
0.05%
0.04%
0.03%
0.04%
0.04%
0.05%
0.03%
0.03%
0.04%
92
0.21%
0.19%
0.15%
0.15%
0.25%
0.15%
0.11%
0.15%
0.05%
0.09%
0.09%
0.08%
0.12%
1.09%
0.11%
0.11%
0.15%
1,t = 36
-11.68%
-16.56%
-23.47%
-14.89%
-3.22%
15.41%
18427.51%
44.08%
20.36%
80.97%
88.02%
64.41%
0.07%
0.07%
0.10%
0.06%
-13.34%
-12.01%
-43.83%
-32.16%
-53.29%
-15.94%
53.96%
12.07%
-33.82%
-34.31%
0.04%
0.08%
-40.70%
-52.03%
12.62%
-19.91%
-48.95%
26.76%
23.96%
1.44%
-53.95%
6.32%
14.76%
-15.34%
-53.59%
-37.29%
-49.20%
-49.35%
-30.88%
1.49%
-11.51%
2.67%
204.25%
174.74%
-6.03%
67.69%
13.17%
-14.50%
54.10%
39.89%
9.85%
-1.89%
-13.52%
-29.29%
189.81%
16.16%
-27.44%
13.00%
-24.95%
-21.61%
23.15%
-33.50%
34.02%
-0.76%
-14.60%
0.09%
0.07%
0.04%
0.04%
0.06%
0.12%
0.08%
0.06%
0.07%
0.06%
0.05%
0.04%
0.04%
0.13%
0.08%
0.03%
0.05%
0.03%
0.03%
0.03%
0.04%
0.04%
0.05%
0.03%
0.03%
0.06%
2.78%
33.72%
12.87%
20.43%
-7.90%
-26.76%
-35.10%
-19.67%
-27.04%
-17.63%
201.48%
144.90%
181.75%
98.40%
-40.76%
1.78%
-23.82%
0.68%
-26.02%
-23.46%
-21.21%
-8.52%
-10.35%
-30.20%
-25.24%
10.85%
15.35%
64.17%
-31.93%
-1.28%
-3.28%
-33.14%
-23.70%
-27.82%
115.25%
117.01%
23.01%
37.54%
7.71%
12.87%
1.28%
3.75%
35.74%
% change at 25 hours
Average
miRNA
miR-23b-3p
miR-582-3p
miR-181a-3p
miR-130a-3p
miR-19a-3p
miR-454-3p
miR-744-5p
miR-15a-5p
miR-5701
miR-122-3p
miR-374a-3p
miR-107
miR-7-Sp
miR-181a-2-3p
miR-193b-3p
miR-483-3p
miR-335-3p
miR-23a-3p
miR-483-5p
miR-548k
let-7g-5p
miR-148b-5p
miR-4454
miR-450b-5p
miR-30d-3p
miR-34a-Sp
miR-1180
miR-720
miR-200b-3p
miR-577
miR-671-3p
miR-193a-5p
miR-574-5p
miR-375
miR-27b-Sp
miR-574-3p
miR-885-Sp
miR-301b
miR-615-3p
miR-125b-2-3p
miR-99a-5p
miR-652-3p
miR-1260b
miR-4455
miR-196b-5p
miR-365a3p/miR-365b-3p
miR-15b-3p
miR-331-3p
% change at 36 hours
Average
Abundance representa expression
rank
doninthe percentat
Average
NT, t =0
hours
NT, t = 24
I, t =24
4.46%
20.17%
-50.72%
47.02%
22.31%
5.49%
-17.55%
0.68%
116.76%
-1.83%
20.04%
1.05%
-35.00%
-19.51%
-23.39%
4.16%
-10.28%
-32.79%
2.56%
-4.33%
-7.93%
-20.59%
50.86%
59.44%
-10.53%
2.50%
3.75%
2.81%
36.48%
-37.97%
79.92%
-1.83%
1.24%
8.42%
2.03%
61.98%
31.47%
-27.17%
86.24%
-27.54%
-17.87%
-5.11%
10.67%
-25.52%
-18.03%
4.73%
51.15%
-12.10%
49.42%
40.61%
12.11%
-28.16%
-3.82%
178.37%
7.40%
67.58%
-1.86%
-19.47%
14.10%
4.26%
4.27%
9.84%
-22.77%
-5.39%
24.98%
-13.68%
-18.73%
-21.10%
71.17%
-2.52%
-17.18%
-23.10%
13.06%
16.14%
-28.47%
31.57%
-16.20%
-11.85%
-23.32%
9.83%
44.94%
-1.78%
-4.14%
63.70%
-12.58%
-8.30%
-15.92%
14.56%
-3.00%
-5.57%
13.59%
34.22%
13.81%
4.70%
3.50%
26.36%
-5.11%
9.65%
210.22%
48.17%
9.65%
24.36%
-22.81%
29.43%
25.95%
3.36%
21.57%
0.94%
-3.09%
17.77%
9.43%
-8.17%
-38.97%
64.36%
1.93%
-9.75%
2.54%
-13.66%
69.53%
-24.86%
45.21%
0.38%
6.20%
22.98%
13.00%
41.26%
15.50%
-17.88%
99.75%
6.86%
10.55%
1.08%
8.24%
-8.24%
8.40%
0.06%
0.03%
0.02%
0.04%
0.09%
0.03%
0.02%
0.02%
0.05%
0.03%
0.03%
0.02%
0.02%
0.01%
0.03%
0.03%
0.01%
0.02%
0.01%
0.01%
0.02%
0.02%
0.02%
0.02%
0.01%
0.02%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.02%
0.01%
0.01%
0.01%
0.01%
0.01%
0.02%
0.01%
0.01%
0.03%
expression
percentat
NT, t =0
hours
NT, t = 36
147.23%
13.88%
-61.06%
85.91%
292.20%
-0.69%
-49.86%
-5.99%
170.40%
4.62%
49.24%
-7.16%
-41.35%
-44.19%
9.11%
27.28%
-58.29%
-15.92%
-56.28%
-38.75%
-24.44%
-25.95%
82.77%
14.03%
-48.45%
5.43%
-50.52%
-34.65%
-25.00%
-58.15%
11.58%
-63.60%
-16.27%
-33.54%
-14.71%
93.94%
17.00%
-19.73%
-26.91%
-54.42%
-49.80%
45.58%
-35.71%
-81.04%
99.11%
123.27%
-0.01%
-16.42%
49.22%
255.16%
-12.38%
-54.79%
-22.60%
31.72%
-24.22%
25.85%
-27.65%
53.25%
-35.63%
48.84%
29.94%
-57.82%
-6.30%
-58.40%
-30.14%
-33.22%
-13.16%
-26.05%
-51.54%
-45.23%
-26.59%
-59.32%
-13.39%
-65.43%
-45.90%
-29.50%
-62.49%
-39.25%
-73.68%
-6.34%
-8.68%
-14.68%
-19.23%
-70.56%
-41.73%
-64.71%
40.08%
-46.61%
-31.78%
132.54%
population
25 hours
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
0.03%
0.03%
0.03%
0.03%
0.03%
0.03%
0.03%
0.03%
0.03%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.03%
0.04%
0.03%
0.04%
0.03%
0.03%
0.03%
0.03%
0.04%
0.02%
0.03%
0.02%
0.03%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.01%
0.02%
0.02%
0.01%
0.02%
0.02%
0.02%
0.01%
0.02%
0.02%
0.01%
0.01%
0.01%
0.02%
0.02%
0.01%
0.02%
0.01%
0.01%
0.01%
0.02%
0.02%
0.01%
126
0.01%
0.01%
-5.99%
10.73%
26.61%
0.01%
-31.96%
-33.84%
127
128
0.01%
0.01%
0.01%
0.01%
-11.69%
26.83%
19.53%
77.71%
50.50%
70.72%
0.01%
0.02%
-26.15%
81.03%
-27.75%
50.20%
I, t =36
36 hours
93
1
160.28%
13.36%
-0.34%
38.70%
254.80%
4.37%
-38.80%
-5.46%
90.00%
-4.39%
82.25%
-1.93%
-0.66%
-21.33%
70.01%
40.60%
-42.99%
17.02%
-52.87%
-22.72%
-14.89%
-2.20%
-5.82%
-18.49%
-13.75%
-16.21%
-56.09%
-26.18%
-43.46%
-29.14%
-36.77%
-61.84%
-24.45%
-49.58%
-26.86%
12.30%
-13.15%
25.23%
-48.31%
-19.65%
-22.99%
33.67%
-41.17%
-39.72%
174.49%
-22.27%
-4.50%
39.02%
% change at 25 hours
Average
IiRNA
miR-126-3p
miR-339-3p
miR-18a-5p
miR-455-5p
miR-1246
miR-190a
riR-130b-5p
miR-216b
miR-374a-5p
miR-3615
miR-125b-5p
let-7c
miR-96-5p
miR-1296
miR-4485
miR-455-3p
miR-16-2-3p
miR-3960
miR-4677-3p
miR-548o-3p
miR-582-5p
miR-424-5p
miR-4746-5p
miR-100-5p
miR-4532
miR-3909
miR-340-5p
miR-664-3p
miR-1908
miR-450a-5p
miR-193a-3p
miR-589-3p
miR-3129-3p
miR-4488
miR-3168
miR-4449
miR-1273g-3p
miR-3656
miR-4516
miR-4492
Abundance representa expression
rank
tion in the percent at
Dopulation 25 hours
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
% change at 36 hours
Average
0.01%
0.01%
0.01%
0.01%
0.00%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.00%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.00%
0.01%
0.01%
0.00%
0.00%
0.01%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
Average
NT, t = 24
I, t =24
expression
percentat
36 hours
NT, t =0
hours
NT, t =36
I, t = 36
-5.83%
2.08%
10.58%
-2.66%
-6.79%
30.51%
-58.44%
-4.39%
36.23%
-10.19%
-6.50%
-2.71%
10.14%
33.43%
14.13%
-24.77%
13.55%
-19.13%
7.99%
2.44%
11.51%
16.37%
10.71%
-25.66%
34.67%
56.72%
6.41%
0.01%
0.01%
6.27%
-5.08%
10.40%
-3.49%
-45.88%
13.54%
11.36%
-49.61%
28.37%
33.25%
-25.36%
16.58%
-7.74%
-29.41%
41.54%
82.24%
1518.31%
-46.66%
-25.91%
-49.26%
19.81%
-37.71%
-45.04%
-48.32%
14.32%
-39.16%
894.44%
56.47%
-47.07%
27926.90%
-31.35%
-45.45%
-16.28%
146.66%
-31.96%
-11.19%
7458.56%
-40.25%
28.40%
233.08%
-61.92%
29.63%
372.57%
22.94%
115.79%
54468.87%
1.57%
5409.20%
3653.23%
471988.77%
69214.84%
308210.32%
-15.62%
-22.95%
37.94%
31.92%
4090.54%
-70.33%
-47.87%
-61.95%
-7.13%
-38.94%
-63.44%
-38.19%
-15.44%
-47.91%
1300.56%
-0.33%
-54.69%
23763.74%
-27.27%
-34.88%
-27.17%
50.67%
-39.86%
-81.70%
15187.59%
-48.22%
-0.86%
209.15%
-65.27%
-35.02%
126.78%
21.08%
78.24%
42850.65%
30.49%
5916.33%
4597.86%
#DIV/0!
85839.40%
#DIV/0!
1.77%
-38.88%
76.72%
51.93%
2948.07%
-31.72%
-41.01%
-12.08%
29.13%
-43.69%
-45.67%
8.12%
-10.85%
-9.47%
1204.83%
4.00%
-52.69%
11712.84%
-29.36%
-14.15%
3.88%
66.42%
-31.85%
-43.18%
7990.47%
-40.18%
58.28%
327.28%
-60.21%
-10.39%
108.49%
-18.90%
76.92%
49504.57%
-25.97%
4671.29%
3711.53%
10265.74%
25331.74%
16578.10%
NT, t =0
hours
-13.44%
-20.03%
16.51%
4.02%
61.80%
16.33%
116.44%
-20.31%
-22.92%
-12.23%
51.95%
3.86%
86.27%
107.06%
7.19%
1.48%
-9.84%
-11.23%
38.04%
51.94%
0.42%
6.19%
-3.65%
-13.89%
-22.33%
45.80%
1113.26%
68.10%
-100.00%
141.77%
10.73%
12.12%
142.24%
-34.39%
-6.01%
-2.21%
35.59%
19.02%
55.92%
28.61%
-0.88%
1.01%
22.73%
-38.27%
30.35%
96.37%
26.87%
31.78%
25.41%
-31.25%
17.97%
64.04%
-20.79%
123.08%
-100.00%
119.72%
28.02%
19.77%
139.87%
-16.04%
1.90%
25.18%
12.05%
25.68%
149.27%
-12.64%
25.98%
21.51%
31.79%
-0.89%
40.44%
11.29%
23.36%
21.91%
-12.92%
-24.49%
-63.45%
50.37%
-22.43%
228.70%
-100.00%
f
94
0.02%
0.02%
0.06%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.05%
0.01%
0.00%
0.08%
0.01%
0.01%
0.01%
0.01%
0.01%
0.00%
0.05%
0.00%
0.01%
0.02%
0.00%
0.01%
0.01%
0.00%
0.01%
0.03%
0.00%
0.01%
0.02%
0.02%
0.02%
0.01%
B.2 High confidence microRNAs for reduced PLSR model
Recently, miRBase included a feature to identify high confidence microRNAs [41].
They use the following criteria to identify high-confidence microRNAs:
-
-
At least 10 reads must map with no mismatches to each of the two possible mature
microRNAs derived from the hairpin precursor.
The most abundant reads from each arm of the precursor must pair in the mature
microRNA duplex with 0-4 nt overhang at their 3' ends.
At least 50% of reads mapping to each arm of the hairpin precursor must have the
same 50 end. The predicted hairpin structure must have a folding free energy of <0.2
kcal/mol/nt.
At least 60% of the bases in the mature sequences must be paired in the predicted
hairpin structure.
Out of 168 microRNAs included in the PCA and PLSR models, 98 met the criteria for
high-confidence. However, a few relevant and validated microRNAs, including miR-122,
miR-221, miR-222 and let-7c are not included in this list. The high-confidence miRNA list is
an evolving endeavor by the Sanger Institute and will probably improve with time.
Table B. 2High-confidence microRNAs included in PCA and PLSR models
High confidence microRNAs in data set
miR-181a-Sp
let-7a-5p
miR-483-3p
miR-210
miR-130b
miR-130b-3p
miR-130b-5p
miR-140-3p
miR-146a-5p
miR-148a-3p
miR-148a-5p
miR-148b-3p
miR-148b-5p
miR-15a-5p
miR-15b-3p
miR-15b-5p
miR-17-5p
miR-181a-3p
miR-769-5p
miR-885-5p
miR-28-3p
miR-28-5p
miR-29a-3p
miR-301a-3p
miR-301b
miR-30a-5p
miR-30b-5p
miR-30c-5p
miR-92b-3p
miR-93-5p
miR-96-5D
miR-1307-5D
miR-21-3p
miR-21-5p
miR-222-3p
miR-25-3p
miR-28
miR-483-5p
miR-548k
miR-548o-3p
miR-561-5p
miR-574-3p
miR-5 7 4 -5 p
miR-582-3p
let-7f-5p
miR-16
miR-26a
miR-92a
miR-941
let-7g
miR-589-3p
miR-589-5p
miR-615-3p
miR-652-3p
miR-671-3p
miR-7-5p
miR-744-Sp
miR-103a-3p
miR-106b-3p
miR-106b-5p
miR-10a-5p
miR-10b-5p
miR-128
miR-1296
miR-1304-3p
miR-1307-3p
miR-20a-5D
miR-450a-5i
miR-582-5p
r
L 95
miR-182-Sp
miR-183-5p
miR-186-5p
miR-18a-5p
miR-1908
miR-190a
miR-191-5p
miR-192-5p
miR-193a-3p
miR-193a-5p
miR-193b-3p
miR-196a-5p
miR-196b-5p
miR-197-3p
miR-19b-3p
miR-30d-3p
miR-30d-5p
miR-30e-3p
miR-30e-5p
miR-335-3p
miR-340-5p
miR-345-5p
miR-34a-5p
miR-374a-3p
miR-374a-5p
miR-378a-3p
miR-423-3p
miR-423-5p
miR-424-5p
miR-4454
miR-450a
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