BIOE598sz: Current Topics in Bioinformatics.

Schedule

Meeting: Spring 2008, Wednesday 3pm – 4:30pm, 3210 Digital Computer Lab.
Credits : 3 graduate hours.

Course Reference number: 46468 

Instructor: Sheng Zhong (szhong AT uiuc DOT edu)
Enrollment Limited to: 15 graduate students. Consent of instructor is required. NO audit.

Content:
This is a seminar course with two aims. 1) Discuss the current Bioinformatics and Systems Biology papers. 2) Improve English presentation and writing skills.

Homework:
A 300-500 word essay reviewing discussed papers due in every 2 weeks.  

Grading:

 

Topics (Lecturer's comments are in green):

1)     From DNA Sequence to Gene Expression.

·        Predicting expression patterns from regulatory sequence in Drosophila segmentation. Eran Segal et al. Nature 2008

January 23. Discussant: Xiaoyi Cao, Chieh-Chun Chen.

·        Predicting Gene Expression from Sequence. Michael A. Beer and Saeed Tavazoie. Cell 2006

January 30. Discussant: Hui Liu, Yanen Li

 

·        Functional Architecture and Evolution of Transcriptional Elements That Drive Gene Coexpression. Cristopher D. Brown, David S. Johnson, Arend Sidow. Science 2007.

2) Evolution of Regulatory Motifs and Gene Expression.

·        Natural selection on gene expression. Yoav Gilad, Alicia Oshlack and Scott A. Rifkin. Trends in Genetics 2006

·        Comparative biology: beyond sequence analysis. Itay Tirosh, Yonatan Bilu, and Naama Barkai. Current opinion in biotechnology 2007.

·        Rewiring of the Yeast Transcriptional Network Through the Evolution of Motif Usage. Jan Ihmels and Naama Barkai et al. Science 2005.

·       Functional evolution of the p53 regulatory network through its target response elements. Anil G. Jegga, Alberto Inga, Daniel Menendez, Bruce J. Aronow, and Michael A. Resnick. PNAS 2008.

·        A genetic signature of interspecies variations in gene expression  Itay Tirosh et al. Nat Genetics 2006

·        A Neutral Model of Transcriptome Evolution. Svante Paabo et al, PloS Biol 2004

·        Intra- and Interspecific Variation in Primate Gene Expression Patterns. Svante Paabo et al, Science 2002
 

·        Expression profiling in primates reveals a rapid evolution of human transcription factors. Yoav Gilad, Kevin White et al, Nature 2006.

·        Multi-species microarrays reveal the effect of sequence divergence on gene expression profiles. Yoav Gilad, Kevin White et al, Genome Research 2005.
 

·        Evolution of primate gene expression. Nature Reviews Genetics 2007

This papers provides a summary of surprising findings in recent years, in particular from Gilad Nature paper:

  1. Gene expression patterns differ substantially among even closely related primate species! This is in sharp contrast of 98% identity of genome sequences of primates (see a series of comments on chimp genome in Science 2005). This argues that the very small amount of sequence changes in primates have induced a surprisingly large amount of expression changes, which is what I will call a general hypothesis. A major assumption behind this hypothesis is that expression is primarily determined by sequence, i.e. cis-effects. One may argue for the trans- effects to be the other factor. It is valid argument, however I hypothesize that even taken the trans- changes into account the general hypothesis still holds.

    How to separate out the trans- effects? It is a difficult question, but the Nature paper from Alex Johnson's group provided a nice route. The analyzed the DNA binding domains of the transcription factors in question. Because the DNA binding domains did not change in a phylogenetic branch that they analyzed, when the transcription factors exist that phylogenetic branch, they reasoned that the trans- effects did not change. (They also analyzed other phylogenetic branches where the TFs were lost and gained, which was a different story.)

    I suggest testing the general hypothesis in the following way. We first focus on the TFBSs that regulate liver gene expression. Liver specific TFBSs are among the best well characterized TFBSs (besides muscle TFBSs.) Gilad Science paper also identified the liver genes that have gone through positive and negative selections. Here we have three categories of liver genes: neutral, positively and negatively selected. We collect all known liver TFBSs from Transfac and literature, most of which were identified in human, mouse and rat. Use our best cross-species sequence alignment strategies to identify their orthologous TFBSs in all primates and other vertebrates with available genomes. Second, we examine whether the TFBS changes are accelerated or decelerated in the same direction as their downstream liver genes' expression. Acceleration and deceleration of TFBS changes are calibrated to rates of changes of 1) neutral sequences and 2) coding sequences of the downstream liver genes.

    If the analysis above offers supporting evidence to the general hypothesis. We can provide an quite different view and computational approach to identify cis-regulatory elements in primates. Potentially, and roughly speaking, we shall probably look for cis-elements that are conserved in vertebrates but quickly evolved in some primate lineages such as in humans.
     

  2. Other important summaries:
    • The extent of transcriptome divergence between species increases monotonically with evolutionary time since their divergence 
    • The extent of divergence of overall gene expression between species differs among tissues and, in a given tissue, parallels the extent of divergence of the amino-acid sequences of proteins expressed in that tissue.

    It looks like that the molecular clock still clicks in transcriptomes! Is this really true? I suspect more studies will come out to address this issue.
     

    • A neutral theory of evolution, where divergence is primarily determined by negative selection and time since divergence, seems to be an adequate and useful null hypothesis for evolutionary analyses of the transcriptome.

    Testing against the neutral evolution theory is a powerful weapon for transcriptome analysis. See application examples in:

    • Genes expressed in the testes have experienced positive selection both with respect to their expression and to their sequences among primates.
    • Gene expression in the brain has diverged less than that of other tissues analysed to date, but a tendency for acceleration of changes on the human lineage relative to the chimpanzee lineage could indicate positive selection.

 

Other related papers to this topic:

·        Neutral and adaptive variation in gene expression. Andrew Whitehead, and Douglas L. Crawford. PNAS 2006

·        Constraint and turnover in sex-biased gene expression in the genus Drosophila. Yu Zhang et al, Nature 2007

·        Tracing the Evolutionary History of Drosophila Regulatory Regions with Models that Identify Transcription Factor Binding Sites -- Dermitzakis et al. 20 (5): 703 -- Molecular Biology and Evolution 2003

·        Comparative Gene Expression Analysis by a Differential Clustering Approach: Application to the Candida albicans Transcription Program PloS Genetics 2005

·        Conservation and evolvability in regulatory networks: The evolution of ribosomal regulation in yeast. Amos Tanay, Aviv Regev, and Ron Shamir, PNAS 2005

·        Evolution of alternative transcriptional circuits with identical logic. A. E.Tsong, B. Tuch, H. Li, A. D. Johnson. Nature 2006 443(7110):415-20.

 

 

3) Regulatory networks.

·        A modular network model of aging. Huiling Xue, Jing-Dong Han et al. Mol Syst Biol 3, (04 Dec 2007).

·        Predictive models of molecular machines involved in Caenorhabditis elegans early embryogenesis. Kristin Gunsalus, Marc Vidal et al. Nature 436 (7052), 861-5 (11 Aug 2005)

·        Systems Biology and Stem Cell Biology. Huck Ng, Sheng Zhong and Bing Lim. Book chapter on Systems Biology, in press.

·        Cross-species analysis of biological networks by Bayesian alignment. Johannes Berg, and Michael Lässig, PNAS 2006

·        The human disease network. Albert-László Barabási et al, PNAS 2007

 

4) Genetic variation

5) Sequencing technology