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Poster Full Abstracts - Systems and Quantitative Biology
Poster board number is above title. The first author is the presenter
363
867C
Causes and Consequences of Genetic Background effects: Re-integrating genetic background into mutational analysis.
Ian M. Dworkin. Dept
Zoology, Michigan State Univ, East Lansing, MI.
In genetic analysis, it is well known that the observed phenotype is not only a function of a given mutant allele, but also the influences of the genetic
background in which it occurs, and the environment in which the organism is reared. Yet, in most genetic analyses such influences are often removed from
consideration (via studying in a single environment in an isogenic background), or worse, ignored. When such influences are explicitly considered, it is
usually only the consequences on the focal mutation that are examined; without consideration of the ordering of allelic effects (allelic series), pleiotropy or
epistasis. I will present work from the lab that has utilized genetic, genomic and bioinformatic approaches to investigate the causes and consequences of
genetic background effects for series of mutations in
scalloped
,
vestigial
and other genes that influence the development of the wing. Results demonstrating
the significant influence of genetic background on epistatic interactions, the ordering of allelic series and transcriptional profiles in the wing imaginal disc
will be discussed. Results from our work to map the modifiers causing the background dependent differences, including re-sequencing efforts will be
presented. These results will be discussed within the context of a broadening appreciation for the influence on genetic background effects, and how it can
significantly aid our understanding and interpretation of genetic analysis.
868A
Different Patterns of H3K27me3 in Four
Drosophila
Species Revealed Through ChIP-seq.
Robert Arthur
1,2
, Matthew Slattery
2
, Rebecca Spokony
2
,
Jennifer Zebia
2
, Lijia Ma
2
, Xiaochun Ni
1,2
, Sarah Suchy
2
, Nicolas Negre
2
, Joelle Perusse
2
, Ilya Ruvinsky
1,2
, Kevin White
1,2
. 1) Ecology and Evolution,
University of Chicago, Chicago, IL; 2) Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL.
Epigenetic regulation exercises profound effects on gene expression throughout development by creating tissue-specific transcriptional profiles. Despite
the significance of epigenetic regulation in guiding development, the evolutionary processes governing it remain obscure. One means of epigenetic
regulation is through the posttranslational modification of histones. Histone 3 lysine 27 trimethylation (H3K27me3) is a histone modification often
associated with gene silencing and thought to be maintained by cis-regulatory modules called Polycomb response elements (PREs). Although H3K27me3 is
a crucial epigenetic regulator, very little is known about its genome-wide evolution. We are investigating patterns of H3K27me3 conservation and
divergence through chromatin immunoprecipitation and sequencing (ChIP-seq). We have gathered data in four species of
Drosophila
:
melanogaster
,
simulans
,
yakuba
, and
pseudoobscura
. For each species, two developmental stages were assayed: the embryo, from 0-4H; and the white prepupal stage.
Comparison of the four species ChIP-seq results reveals that patterns of H3K27me3 are strongly conserved. Genes with H3K27me3 signatures overlapped
substantially between different developmental stages both within and between species. In addition, we observed examples of conserved exon-specific
H3K27me3. We plan to examine how conservation of this crucial histone modification relates to gene expression (as measured by RNAseq), as well as
conservation of the underlying methylated sequence and the most proximal PRE. We are also extending this method to examine other epigenetic signals in a
greater number of species and developmental stages. ChIP-seq assays against other factors, including those bound to PREs, will enable a better grasp of how
epigenetic marks are created and maintained, both developmentally and evolutionarily.
869B
Correlating gene expression patterns with gene, protein, and RNA interaction networks in Drosophila.
Thilakam Murali, Russell Finley. Ctr
Molecular Medicine and Genetics, Wayne State Univ Med School, Detroit, MI.
A systems-level understanding of cellular processes requires analysis of the emerging flood of data on how genes and their products (RNAs and proteins)
interact. We developed DroID, the Drosophila Interactions Database (www.DroIDb.org), to be a comprehensive resource for interaction data, including
transcription factor-gene (TF-gene) interactions, microRNA-gene (miR-gene) interactions, genetic interactions, experimentally determined protein-protein
interactions (PPI) and PPI predicted from data in other organisms (interologs). DroID also has genome-wide expression and localization data that can all be
used to search and filter interaction networks. The majority of the interactions in DroID, however, are determined by methods that are independent of gene
expression patterns in vivo; i.e., the data are a composite of potential interactions. To help identify general characteristics of gene networks that operate in
different spatiotemporal contexts, we are correlating the interactome data with recent tissue-specific and developmental stage-specific expression data from
FlyAtlas and the modENCODE project. Our broad aim is to understand how the interactome is modified by gene expression patterns. Initially, we ranked
gene expression on a scale from tissue- or stage-specific to ubiquitous. Interestingly, ubiquitously expressed genes have about ten times more interactions
among themselves than non-ubiquitously expressed genes. In addition, the tissue- or stage-specific genes were found to interact more with ubiquitously
expressed genes than among themselves or with the other non-ubiquitous genes. The genes that were neither ubiquitous nor tissue-specific tend to interact to
a lesser degree among themselves but more with the ubiquitous and tissue-specific genes. These findings are consistent with a hub of widely expressed genes
to which are attached various tissue or stage specific genes. To facilitate additional analyses of gene expression and interactome data we present an approach
to filter interaction networks based on normalization of expression data.
870C
Prediction of Orthologous Gene Function: Experimental Verification of
I
D
Test Results.
Anna James
1
, Sudhindra R. Gadagkar
1
, Ellen D. Tarr
2
, Gerald
B. Call
3
. 1) Department of Biomedical Sciences, College of Health Sciences, Midwestern University, Glendale, AZ; 2) Department of Microbiology &
Immunology, Arizona College of Osteopathic Medicine, Midwestern University, Glendale, AZ; 3) Department of Pharmacology, Arizona College of
Osteopathic Medicine, Midwestern University, Glendale, AZ.
The Disparity Index (
I
D
) statistic of Kumar and Gadagkar (2001) measures the observed difference in evolutionary (e.g., nucleotide substitution) patterns
between two orthologous molecular sequences, and the associated Monte Carlo test assesses the statistical significance of any observed difference. Thus, the
(
I
D
) test can be used to determine the homogeneity or heterogeneity of nucleotide/amino acid substitution pattern for a given gene between two species after
they have diverged from a common ancestor. While a homogeneous substitution pattern signifies that the given gene is still essentially the same between the
two species, a heterogeneous pattern implies that the evolutionary constraints have diverged between the two species for that gene. A reasonable inference
from a heterogeneous result is that the two orthologous genes have also diverged in function. In this collaboration among three labs (a computational lab, a
fly lab and a worm lab), we test the accuracy and reliability of the (
I
D
) test, by assaying function in the area of general anesthesia response