Poster Abstracts
AR-BIC First annual conference - March 11-12, 2015
Advancing
Regulatory Science through Bioinformatics
Huixiao Hong, Roger Perkins and Weida Tong
Division of Bioinformatics and Biostatistics, National Center for Toxicological
Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson,
AR 72079, US
In
2010, the US FDA launched its Advancing Regulatory Science (ARS)
initiative aimed at developing new tools, standards,
and approaches to assessing
safety, efficacy, quality, and performance across FDA-regulated products.
The initiative identifies eight scientific areas that affect multiple
regulated product domains or human populations, where bioinformatics
play paramount roles. The Division of Bioinformatics and Biostatistics
at FDA’s Center for Toxicological Research (NCTR) engages in bioinformatics
applicable to such areas as biomarker development and validation, drug
safety and repurposing, and personalized medicine. This poster will highlight
selected bioinformatics research as well as selected databases and software
tools that have been developed both in past years and more recently in
support of FDA regulatory sciences. The DBB has led a large international
consortium for the past eight years that has assessed the reliability
of clinical and toxicological biomarkers derived from emerging microarray
and next generation sequencing. Knowledge bases have been developed that
aggregate diverse data associated with a disease, toxicity or phenotype,
providing a means for mechanistic studies and development of predictive
models. The Liver Toxicity Knowledge Base integrates in vitro, in vivo,
gene expression data and textual data. The Endocrine Disruptor Knowledge
Base contains in vitro and in vivo data for thousands of chemicals to
build models to predict endocrine activity mediated by estrogen and androgen
hormone receptors based solely on chemical structure. The Food-Borne
Pathogen Genomics Knowledge Base provides tools to detect and characterize
microbial isolates from gene expression data during pathogen outbreaks.
ArrayTrack is a genomics tools widely used within FDA, as well as the
public, private and academic research community worldwide. ArrayTrack
provides an integrated means to manage, analyze and interpret omics data.
It contains many statistical and visualization tools as well as libraries
for gene and protein function and biological pathways. FDALabel is a
web-based database containing the entire set of 40,000 FDA-approved drug
labels. It contains a powerful and flexible search capability, and much
other functionality valuable to researchers, regulators, drug developers
and clinicians. FDALabel will provide an improved bridge for transparent
drug safety knowledge exchange between the public and FDA. A common element
of the databases and bioinformatics tools cited above is that they either
are or will be openly available on the Internet, including an FDA external
Cloud when available, thus advancing FDA data liberation. Many of NCTR’s
bioinformatics tools can be accessed through the following link: FDA
Bioinformations Tools.
AR-BIC-1
Tao Chen1, Fanxue Meng1, Michael Hackenberg2, Zhiguang Li1, Jian Yan1,
1Division of Genetic and Molecular Toxicology, National Center for Toxicological Research, Food and Drug Administration, Jefferson.
2Dpto. de Genetica, Facultad de Ciencias, Universidad de Granada, Granada, Spain
MicroRNAs (miRNAs) are small non-coding RNAs that regulate a variety
of biological processes. The version of the miRBase database (Release
18) includes 1,157 mouse and 680 rat mature miRNAs. Only one new rat
mature miRNA was added to the rat miRNA database from version 16 to version
18 of miRBase, suggesting that many rat miRNAs remain to be discovered.
Given the importance of rat as a model organism, discovery of the completed
set of rat miRNAs is necessary for understanding rat miRNA regulation.
In this study, next generation sequencing (NGS), microarray analysis
and bioinformatics technologies were applied to discover novel miRNAs
in rat kidneys. MiRanalyzer was utilized to analyze the sequences of
the small RNAs generated from NGS analysis of rat kidney samples. Hundreds
of novel miRNA candidates were examined according to the mappings of
their reads to the rat genome, presence of sequences that can form a
miRNA hairpin structure around the mapped locations, Dicer cleavage patterns,
and the levels of their expression determined by both NGS and microarray
analyses. Nine novel rat hairpin precursor miRNAs (pre-miRNA) were discovered
with high confidence. Five of the novel pre-miRNAs are also reported
in other species while four of them are rat specific. In summary, 9 novel
pre-miRNAs and 14 novel mature miRNAs were identified via combination
of NGS, microarray and bioinformatics high-throughput technologies.
AR-BIC-2
Halil Bisgin, Binsheng Gong, Yuping Wang, Weida Tong
Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration
Background: Development of next-generation sequencing (NGS) techniques
opened a new era in genomic research and led several studies in RNA-Seq.
Despite the excitement, concerns have arisen about profiling tools and
defining the standards. In recent years, FDA SEQC consortium took an
initiative to address technical and statistical challenges in RNA-seq.
However, similar issues have not been extensively studied for miRNA-Seq
in the research community.
Method: We investigated the effect of parameter space on downstream analysis
by exploring four miRNA-Seq profiling tools (mirDeep2, mirExpress, miRNAkey,
sRNAbench). Given the mirRNA-seq data generated from rat liver samples
that were treated by Thioacetamide in four time points and three dose
levels, we first compared the variance in the number of differentially
expressed miRNAs (DEMs) for each tool with their own parameters. mirDeep2
and sRNAbench were further exploited with common parameters (genome mapping,
windowing, quantification) to study the detection sensitivity and DEM
variability along with normalization choice.
Results: The analysis showed that under the same parameters sRNAbench
detected more miRNAs most of which were also detected by mirDeep2. Under
the same normalization method, mirDeep2 had more DEMs which showed higher
overlap ratio with sRNAbench compared to detection sensitivities. While
windowing introduced more variance in the detection, genome mapping was
also effective in the variability of DEMs. For higher doses and longer
durations, mirDeep2 was less sensitive to parameter changes which resulted
in more agreement on DEMs within itself. Profiling parameters did not
exceed 8%, when time, dose, and time-dose interaction were considered
in the variance. A change in the normalization step affected the DEMs
close to treatment factors, but the trend across time and dose remained
similar.
Conclusion: Results indicated that for the given normalization method,
profiling parameters had limited impact on the downstream analysis. On
the other hand, normalization considerably changed the number of DEMs,
but choice of normalization still allowed time-dose pattern to follow
similar trends which was the sign of treatment effect.
AR-BIC-3
Development of a competitive
molecular docking approach for predicting estrogen receptor agonists
and antagonists
Hui Wen Ng, Weida Tong and Huixiao Hong
Division of Bioinformatics and Biostatistics, National Center for Toxicological
Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson,
AR 72079
Molecular docking is a well-established molecular modeling
technique commonly used in ligand screening and drug design. This method
attempts
to predict the binding mode and molecular interactions between a protein
and a ligand as well as rank the predicted poses with scoring functions.
The protein-ligand association in vivo is characterized by a dynamic
process whereby protein-ligand binding is accompanied by a conformational
change in the complex, a phenomenon commonly referred to as “induced-fit”.
However, due to high computational costs, fully flexible docking remains
impractical. In light of this, rigid docking and limited flexible docking
become the most commonly practiced methods. The estrogen receptors (ERs)
adopt distinctly different conformations upon binding to the agonists
and antagonists. Using the ER subtype a agonist and antagonist conformations,
we designed an in silico approach that more closely mimics the biological
process, and used it to differentiate the agonist versus antagonist status
of potential binders. The ability of this approach was first evaluated
using true agonists and antagonists extracted from the crystal structures
available in the protein data bank (PDB), and then further validated
using a larger set of ligands from the literature. The usefulness of
the approach was demonstrated with enrichment analysis in data sets with
a large number of decoy ligands. The performance of individual agonist
and antagonist docking conformations were found comparable to similar
models in the literature. When combined in a competitive docking approach,
they provided the ability to discriminate agonists from antagonists with
good accuracy, as well as the ability to efficiently select true agonists
and antagonists from decoys during enrichment analysis. In conclusion,
this approach offers potential applications not only in drug discovery
projects in the pharmaceutical industry but also in the screening of
potential endocrine disrupting compounds (EDCs) by regulatory authorities
to perform risk assessments on potential EDCs.
AR-BIC-4
Collection and molecular docking identification of associations between drugs and class I human leukocyte antigens for predicting idiosyncratic drug reactions
Heng Luo1,2, Huixiao Hong1
1 National Center for Toxicological Research, US Food and Drug Administration,
2 University of Arkansas at Little Rock/University of Arkansas for
Medical Sciences joint Bioinformatics program
Corresponding to: Huixiao.Hong@fda.hhs.gov
Idiosyncratic
drug reactions (IDRs) are rare, somewhat dose-independent, patient-specific
and hard
to predict. Human leukocyte antigens (HLAs)
are the major histocompatibility complex (MHC) in humans, are highly
polymorphic and are associated with specific IDRs. Therefore, it is
important to identify potential drug-HLA associations so that individuals
who would
develop IDRs can be identified before drug exposure. We harvested the
associations between drugs and HLAs from the literature and built up
a database named HLADR. Molecular docking was used to explore the known
associations. From the analysis of docking scores between the 17 drugs
and 74 class I HLAs, it was observed that the significantly associated
drug-HLA pairs had statistically lower docking scores than those not
reported to be significantly associated (t-test p < 0.05). This indicates
that molecular docking can be utilized for screening drug-HLA interactions
and predicting potential IDRs, and may improve drug safety and the implementation
of personalized medicine. Examining the binding modes of drugs in the
docked HLAs suggested several distinct binding sites inside class I HLAs,
expanding our knowledge of the underlying interaction mechanisms between
drugs and HLAs.
AR-BIC-5
Deciphering adverse outcome pathways through network analysis of ToxCast data
Hao Ye 1, Heng Luo2, Hui Wen Ng1, Weigong Ge1, Weida Tong1, Huixiao
Hong1*
1Division of Bioinformatics and Biostatistics, National Center for
Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR
Road, Jefferson, AR 72079; 2University of Arkansas at Little Rock/University
of Arkansas for Medical Sciences Bioinformatics Graduate Program, Little
Rock, Arkansas, AR 72204
*Correspondence should be addressed to Dr. Huixiao Hong at huixiao.hong@fda.hhs.gov
ToxCast data have been demonstrated to be efficient in characterizing
the toxicological profiles of environmental chemicals. An adverse outcome
pathway (AOP) is a group of molecular events related at higher levels
of biological organizations (e.g. cell or tissue) that ultimately lead
to an adverse outcome. Network analysis was frequently used to investigate
the group properties of networks such as social network, electronic
commerce network, and biological network. We first constructed a network
in which the assays and chemicals assayed in ToxCast data were treated
as nodes and the positive assay results were used to connect the nodes.
We then applied a network analysis to inspire the understanding of
ToxCast data and to identify potential AOPs. We also demonstrated the
activity data of untested chemicals in the ToxCast assays could be
predicted using the network analysis. We found the compound-assay network
could be decomposed into seven densely connected modules based on its
topological properties. Moreover, each of the seven modules was associated
with different AOPs. For example, most of ER, AR, and GR related assays
were significantly enriched in module one. We will present our results
and discuss the implications, limitations and perspectives of the network
analysis on ToxCast data.
AR-BIC-6
Ensemble Survival Trees for Identifying Subpopulations in Personalized Medicine
Yu-Chuan Chen James J. Chen
Recently,
personalized medicine has received a great attention to improve safety
and effectiveness in
drug development. Personalized medicine
aims to provide medical treatment that is tailored to the patient’s
characteristics such as genomic biomarkers, disease history, etc.,
so that the benefit of treatment can be optimized. Subpopulations identification
is to divide patients into several different subgroups where each subgroup
corresponds to an optimal treatment. For two subgroups, traditionally
multivariate Cox proportional hazards model is fitted and used to calculate
the risk score when outcome is survival time endpoint. Median is commonly
chosen as the cutoff value to separate patients. Here we propose a
novel tree-based method that adopts the algorithm of relative risk
trees to identify subgroup patients. After growing a relative risk
tree, we apply ??-means clustering to group the terminal nodes based
on the averaged covariates. We adopt an ensemble Bagging method to
improve the performance of a single tree since it is well known that
the performance of a single tree is quite unstable. A simulation study
is conducted to compare the performance between our proposed method
and the multivariate Cox model. The applications of our proposed method
to three public cancer data sets are also conducted for illustration.
AR-BIC-7
Collection and molecular docking identification of associations between drugs and class I human leukocyte antigens for predicting idiosyncratic drug reactions
Heng
Luo1,2, Huixiao Hong1
1 National Center for Toxicological Research, US Food and Drug Administration,
2 University of Arkansas at Little Rock/University of Arkansas for Medical
Sciences joint Bioinformatics program
Corresponding to: Huixiao.Hong@fda.hhs.gov
Idiosyncratic
drug reactions (IDRs) are rare, somewhat dose-independent, patient-specific
and hard to predict. Human leukocyte antigens (HLAs) are the major
histocompatibility complex (MHC) in humans, are highly polymorphic
and are associated with specific IDRs. Therefore, it is important
to identify potential drug-HLA associations so that individuals who
would develop IDRs can be identified before drug exposure. We harvested
the associations between drugs and HLAs from the literature and built
up a database named HLADR. Molecular docking was used to explore
the known associations. From the analysis of docking scores between
the 17 drugs and 74 class I HLAs, it was observed that the significantly
associated drug-HLA pairs had statistically lower docking scores
than those not reported to be significantly associated (t-test p < 0.05).
This indicates that molecular docking can be utilized for screening
drug-HLA interactions and predicting potential IDRs, and may improve
drug safety and the implementation of personalized medicine. Examining
the binding modes of drugs in the docked HLAs suggested several distinct
binding sites inside class I HLAs, expanding our knowledge of the
underlying interaction mechanisms between drugs and HLAs.
AR-BIC-8