New Approaches to Gene Co-expression Network Analysis

  An important new paper on the methodology for doing gene co-expression network analysis was recently published in PLoS ONE by Kumari et al. (2012).  The paper is entitled "Evaluation of Gene Association Methods for Coexpression Network Construction and Biological Knowledge Discovery".

The authors perform a comparative analysis of several different approaches for constructing co-expression networks.

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Abstract:
Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical.

Methods and Results: In this study, we compared eight gene association methods – Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding’s D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson – and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods.

Conclusions: We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction.

Dr. Coni Horndli Receives Prestigious Swiss Fellowship!!

Dr. Coni Horndli has been awarded a prestigious fellowship from the Swiss National Science Foundation.  Dr. Horndli is a postdoctoral fellow in the Gregg Lab developing novel approaches to study genetic and epigenetic pathways in the brain that modulate complex feeding and foraging behaviors.  She has a particular interest in molecular mechanisms that influence stress and anxiety.  Dr. Horndli's work is anticipated to transform our understanding of the mechanisms in the brain that contribute to susceptibility to eating disorders, stress and anxiety-related disorders, and depression.

IGF2:IGF2R Evolution

By:  Dr. Coni Horndli

An Exon Splice Enhancer Primes IGF2:IGF2R Binding Site Structure and Function Evolution

Christopher Williams,1* Hans-Jürgen Hoppe,2* Dellel Rezgui,2 Madeleine Strickland,1 Briony E. Forbes,3 Frank Grutzner,3 Susana Frago,2 Rosamund Z. Ellis,1 Pakorn Wattana-Amorn,1 Stuart N. Prince,2 Oliver J. Zaccheo,2 Catherine M. Nolan,4 Andrew J. Mungall,5 E. Yvonne Jones,6 Matthew P. Crump,1† A. Bassim Hassan2†

ABSTRACT

Placental development and genomic imprinting coevolved with parental conflict over resource distribution to mammalian offspring. The imprinted genes IGF2 and IGF2R code for the growth promoter insulin-like growth factor 2 (IGF2) and its inhibitor, mannose 6-phosphate (M6P)/IGF2 receptor (IGF2R), respectively. M6P/IGF2R of birds and fish do not recognize IGF2. In monotremes, which lack imprinting, IGF2 specifically bound M6P/IGF2R via a hydrophobic CD loop. We show that the DNA coding the CD loop in monotremes functions as an exon splice enhancer (ESE) and that structural evolution of binding site loops (AB, HI, FG) improved therian IGF2 affinity. We propose that ESE evolution led to the fortuitous acquisition of IGF2 binding by M6P/IGF2R that drew IGF2R into parental conflict; subsequent imprinting may then have accelerated affinity maturation.

COMMENT

This report published by Matthew Crump’s and Bassim Hassan’s groups this week in Science analyses the evolutionary molecular changes, which led to high affinity binding of IGF2R to IGF2 in mammals but not birds and reptiles. IGF2 and IGF2R are two of the roughly 100 canonically imprinted genes found in mammals, with IGF2 expressed only from the paternal allele and IGF2R only from the maternal allele. In mice, deletion of the maternal IGF2R gene results in overly large offspring while deletion of the paternal IGF2 gene results in dwarf offspring. In humans, only IGF2 is imprinted but not its receptor. Activation of the maternal IGF2 allele causes Beckwith-Wiedemann syndrome, which is characterized large body size at birth and an increased risk for childhood cancer. The reciprocal expression of IGF2 and IGF2R underscores the parental conflict over the distribution of resources to their offspring. This hypothesis is based on the theory that mothers want to distribute their resources equally to all their current and future offspring, while fathers favor the maternal investment into the current offspring.

This study correlates the appearance of IGF2/R with the occurrence of their monoallelic expression. Specifically, Williams et al. show that binding appeared in all primitive mammals, while imprinting is only found in theria, such as rodents, kangaroos and opossums. Therefore, the authors hypothesize that the evolution of IGF2/R imprinting was facilitated by the appearance of their molecular binding, which may conversely have accelerated the selection for improved regulation of IGF2 through IGF2R.

This report thoroughly reveals the structural changes that lead to IGF2:IGF2R complex formation but falls short on explaining the mechanism of how IGF2/R binding facilitates genomic imprinting of these two genes.

Innovator of the Year 2012 - Sridhar Vukkadapu

2012 Innovator of the Year Award

The 2012 Gregg Lab Innovator of the Year is Sridhar Vukkadapu.  Sridhar is completing his Master's Degree in computer science and has worked in the lab for one year.  He has developed a software pipeline for the analysis of allele-specific gene expression using high-throughput sequencing.  The pipeline, called PARENT SEQ'R, is a full suite of code written in C++, Python, Perl and Processing that manages the alignment, processing, analysis and visualization of RNA-Seq data to study allele-specific gene expression.  It is a tour de force achievement that opens up new ways of studying gene expression data and has revealed many new discoveries in the lab.  We look forward to making the pipeline and data visualization software publicly available in the future following publication!!