Improving Gene Function Prediction Using Gene Neighborhoods

1
Improving Gene Function Prediction Using Gene
Neighborhoods

• Kwangmin Choi
• Bioinformatics Program
• School of Informatics
• Indiana University, Bloomington, IN

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Introduction PLATCOM (A Platform for
Computational Comparative Genomics)

• PLATCOM is a system for the comparative analysis
  of multiple genomes.
• PLATCOM consists of 3 components
• Databases of biological entities
• e.g. fna, faa, ptt, gbk
• Databases of relationships among entities
• e.g. genome-genome, protein-protein pairwise
  comparison
• Mining tools over the databases
• The web interface of PLATCOM system is located at
  http//biokdd.informatics.indiana.edu/kwchoi/platc
  om/

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PLATCOM Web Interface Frontpage of Genome Plot
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Background What is operon ? http//biocyc.org15
55/ECOLI/new-image?objectTranscription-Units

• The operon structure was found in 1960 by 2
  French biologists. Jacob,F. and Monod,J. (1961)
  Genetic regulatory mechanisms in the synthesis of
  proteins. J. Mol. Biol., 3, 318356.
• An operon is a group of genes that encodes
  functionally linked proteins. Its components are
  
• Adjacent (200-300 nt)
• On the same strand ( or -)
• Co-expressed by one promoter.

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Background How to identify or predict operon
structure?

• When a promoter and terminator are known
• Gene clusters Transcription Units
• Classical concept of operon
• When a promoter is not known
• Gene clusters Directrons
• Hypothetical operon candidates
• Depending on direction and proper intergenic
  distance (200-300 nt)
• Computational methods have been developed to find
  gene clusters in bacterial genomes.

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PCBBH and PCH R.Overbeek et al. PNAS, 1999,
Vol.96, pp.2896-2901
PCBBH Pair of Close Bidirectional Best Hits BBH
Bidirectional Best Hits PCH Pair of Close
Homologs COG Clusters of Orthologous Genes
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Background Über-operon P.Bork et al. Treds.
Biochem. Sci., Vol. 25, pp. 474-479

• Über-operon A set of genes with a close
  functional and regulatory contexts that tends to
  be conserved despite numerous rearrangements.
• This concept focus on the functional themes of
  operons, not a specific genes or gene order.

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Background Why gene clusters are conserved ?

• Certain operons, particularly those that encode
  subunits of multiprotein complexes (e.g.
  ribosomal proteins) are conserved in
  phylogenetically distant bacterial genomes.
• These gene clusters might have been conserved
  since the last universal common ancestor. Why?
• Selfish-operon hypothesis Horizontal transfer of
  an entire operon is favored by natural selection
  over transfer of individual genes because
  co-expression and co-regulation are preserved.

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Background Problems in Operon Prediction.

• Over 150 genomes have been fully sequenced until
  today, but The biological functions of some genes
  are still unknown.
• There is only a few promoter detection
  algorithms, but they are not fully satisfactory.
• In many cases, genomic data files do not provide
  full information of genes and their products. (
  e.g. gene name, COG, PID.)
• Operon tends to undergo multiple rearrangements
  during evolution.
• As a result, gene order at a lever above is
  poorly conserved. (e.g. genes involved in de novo
  purine synthesis)

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Background Problems in Computational
Algorithms to Predict Operons

• Direct Signal Finding
• Experiment-based approach
• Transcription promoters (5-end) and terminators
  (3-end) were searched.
• Only be effective for species whose transcription
  signals are well known, E.coli.
• Combination of gene expression data, functional
  annotation and other experimental data.
• Literature-based approach
• Primarily applicable to well studied genomes such
  as E.coli, because data files are incomplete for
  other genomes.
• In many cases, genomic data files do not provide
  full information of genes and their products. (
  e.g. gene name, COG, PID.)

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Procedure

• As a part of PLATCOM project, an integrated whole
  genome analysis system was built on BIOKDD
  server.
• Web interface for all-to-all pairwise comparison
  DB and tools are also provided.
• Several tools for multiple genomes analysis were
  written in Perl and then gene neighborhoods was
  reconstructed from the clustering data.
• My gene clustering algorithm was used to
  compensate the defect of the literature-based
  approach.
• Connected gene neighborhoods were analyzed to
  predict gene function and functional coupling
  between clusters.

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Materials/ Tools

• Raw Data
• 22 genomes were chosen for this study. (14
  groups)
• Protein-Protein Pairwise Comparison Data
• e.g. http//biokdd.informatics.indiana.edu/kwchoi/
  Thesis/L42023.faa.U00096.faa.cmp.txt
• PTT files from NCBI site
• e.g. http//biokdd.informatics.indiana.edu/kwchoi/
  Thesis/U00096.ptt.txt
• Data Generated by Web Tools
• Gene Clustering Data (based on sequence homology)
  
• e.g. http//biokdd.informatics.indiana.edu/kwchoi/
  Thesis/clustering_13321_23_750.txt
• Gene Clusters generated from PTT file (given
  intergenic distance)
• e.g. http//biokdd.informatics.indiana.edu/kwchoi/
  Thesis/candidates_22211.htm
• E. coli database for reality check
• http//biocyc.org/
• http//ecocyc.org/

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Genomes http//www.infobiogen.fr/services/deambulu
m/english/genomes2a.html
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Procedure My Approach to reconstruct Genomic
Neighborhoods

• The idea underlying this study is that
• Different genomes contain different, overlapping
  parts of evolutionarily and functionally
  connected gene neighborhoods
• By generating a Tiling Path, the entire
  neighborhood can be reconstructed.
• Genomic context of well-known genome (e.g. E.coli
  ) is used as a contextual framework.
• Start with looking at this framework and then
  search a group of similar gene neighborhoods in
  the target genomes.
• Genomic context means the pattern of series of
  COG. If COG is not given, we can predict the
  function of a unknown gene based on my gene
  clustering data.
• We can also identify some Hitchhikers.
  Hitchhikers are inserted genes that are
  originated from different contexts/themes.

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Tiling PathV.Koonin et al. Nucleic Acids
Research, 2002, Vol.30, No.10, pp. 2212-2223
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Gene Neighborhoods
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Results

• Case 1
• Relationship between Gene Order and Phylogenetic
  Distance
• Case 2
• One theme Typical Operon (rbs operon)
• Reconstruct gene neighborhoods
• Find missing components from the reconstructed
  gene clusters.
• Case 3
• Two or more themes Functional Coupling ?
• Find genomic hitchhikers
• Predict gene function of uncharacterized protein
• Predict functional coupling

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Case 1 Gene Order and Phylogenetic Distance

• If gene order of two genome is well conserved,
  the sequence of homologs should appear as a line
  on the genome comparison diagonal plot.
• What is the relationship between phylogenetic
  distance and the conservation of gene order?

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Phylogenetic TreeV.Daubin et al. Genome
Research, Vol 12, Issue 7, 1080-1090
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Genome Comparison Diagonal Plot
Phylogenetically-Distant Species (Z-score over
500)
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Genome Comparison Diagonal Plot
Phylogenetically-Close Species (Z-score gt 1000)
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Fragmented Gene Clusters
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Case 1 Conclusion

• Gene order in phylogenetically-distant species
  are poorly conserved.
• But this observation does not mean that gene
  order is conserved very well among the
  phylogenetically-close species.
• In case of very close species (e.g. E.coli vs.
  H.influenza), gene orders are completely
  scattered.
• In most cases, only a small number of genes are
  observed as a short line or cluster and we may
  consider it as a putative operon.
• In next step, this possibility will be
  investigated deeply.

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Case 2 Rbs Operon (Typical Operon)

• Theme Ribose transport across membrane
• COG1869 D-ribose high-affinity transport system
  membrane-associated protein
• COG1129 ATP-binding component of D-ribose
  high-affinity transport system
• COG1172 D-ribose high-affinity transport system
  
• COG1879 D-ribose periplasmic binding protein
• COG0524 ribokinase
• COG1609 regulator for rbs operon
• http//biocyc.org1555/ECOLI/new-image?typeOPERON
  objectTU00206

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Case 2 Rbs OperonZ-score over 750,
Intergenic Distance 300
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Case 2 Conclusion

• All components are involved in ribose transport
  across bacterial cell membrane
• In Rbs operon system, gene order pattern is
  1869-1129-1172-1879-0524-1609.
• 10 out of 22 genomes have this operon system.
• Exceptsome cases, this gene order pattern is
  conserved very well.
• So it is possible that there exists a kind of
  General Contextual Framework of gene order.

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Case 3 Functional Coupling of 2 or more themes

• Theme 1 Transcription
• COG0779 Uncharacterized Conserved Protein
• COG0195 Transcription elongation factor
• COG2740 Predicted nucleic-acid-binding protein
  (transcription termination?)
• Theme 2 Translation
• COG1358 Ribosomal protein S17E
• COG0532 Translation initiation factor 2 (GTPase)
  
• COG1550 Uncharacterized Conserved Protein
• COG0858 Ribosome-binding factor A
• COG0184 Ribosomal protein S15P/S13E
• COG0130 tRNA Pseudouridine synthase
• Hitchhiker ?
• COG0196 FDA Synthase (Hitchhiker?)
• http//biocyc.org1555/ECOLI/new-image?typeOPERON
  objectTU341

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Case 3 Functional CouplingZ-score over 750,
Intergenic Distance 300
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Case 3 Conclusion

• Functional Coupling
• In bacteria, transcription, translation and RNA
  modification/degradation are coupled and the
  advantages of co-regulation the corresponding
  genes are obvious.
• COG0779(Uncharacterized) is almost inseparable
  from the COG0195(Transcription Elongation
  Factor), so it is likely to be a functional
  partner of COG0195.
• Hitchhiker
• The association of the COG0196(FDA synthase) is
  not as tight as the connections between the genes
  belonging to the theme.
• Gene function prediction
• The functions of 3 genes in AE0004092 genomes can
  be predicted by reading genomic context.

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Conclusion

• Genome Comparison Diagonal Plot visualizes the
  sequence comparison of 2 genomes. It is a simple
  tool, but presents a very strong intuition to
  understand the genome structure.
• Conserved gene neighborhoods reconstructed from
  many genomes by the Tiling Path Method can be
  used to predict the functions of uncharacterized
  genes and functional coupling between
  well-characterized genes in those genomes.
• Ultimately, We can use this methods to
  reconstruct metabolic and functional subsystems.

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Acknowledgements

• Haifeng Zhao
• Genome Pairwise Comparison DB
• Scott Martin
• Server Management and Technical Suppor
• Dr. Sun Kim
• Graduate Advisor and P.I.