Bioinformatics Computational methods to discover ncRNA in bacteria

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BioinformaticsComputational methods to discover
ncRNA in bacteria

• Ulf Schmitz
• ulf.schmitz_at_informatik.uni-rostock.de
• Bioinformatics and Systems Biology Group
• www.sbi.informatik.uni-rostock.de

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Outline

• Problem description
• Streptoccocus pyogenes
• The RNome, transcriptome
• Characteristics of bacterial ncRNA
• Approaches to find fRNA
• Conclusion / Outlook

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Streptococcus pyogenes

• important human pathogen (group A streptococcus
  or GAS)
• causes following diseases
• pyoderma (111 million cases/year)
• pharyngitis (616 million cases/year and 517,000
  deaths/year)

• completely adapted to humans as its only natural
  host
• causes purulent infections of the skin and mucous
  membranes and rarely life-threatening systemic
  diseases

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Streptococcus pyogenes

• varies in multiplication rate -gt associated with
  type of infection
• to understand the regulation, one studied the
  growth-phase regulatory factors and gene
  expression in response to specific environmental
  differences within the host
• a novel growth phase assosiated
  two-component-type regulator was identified
• fasBCA operon, present in all 12 tested M
  serotypes
• contained two potential HPK genes (FasB, FasC)
  and one RR (FasA)
• shows its maximum expression and activity at the
  transition phase
• and to potentially support the aggressive
  spreading of the bacteria in its host

HPK Histidine protein kinase RR response
regulator
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Streptococcus pyogenes

• downstream of the fas operon they identified a
  300 nucleotide transcript (fasX)
• not encoding for a peptide/protein
• but also growth phase related
• main effector molecule of fas regulon
• ncRNA or fRNA

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ncRNA
fasX
gltX-L
fasB
fasC
fasA
rnpA-L
tt
pfasX
prnpA
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RNome or transcriptome
putative gene expression regulators (also protein
interaction and housekeeping ncRNAs where found)
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RNome or transcriptome
types of RNA
Non-coding RNA (ncRNA) genes produce functional
RNA molecules rather than encoding proteins and
here are the nominees
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Functions of ncRNA
target mRNAs via imperfect sequence
complementarity

• binding may result in
• blockage of ribosome entry
• (translation repression)
• melting of inhibitory
• secondary structures
• (translation activation)

dissolving fold the fold back structure
loop-loop kissing complex
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Streptococcus pyogenes genomes
Genome Info Features
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Intergenic sequence inspector (ISI)
Bacterial genomes database
Annotated genome
IGR databank
Filtered IGR databank
BLAST results
Aligned features
Sequence features
Final results
IGR extractor
IGR filtering
BLAST
BLAST Analyser
Genview
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Characteristics of bacterial ncRNA

• intergenic sequence/structure conservation
  between related
• genomes
• encoded by free-standing genes, oriented in
  opposite
• fashion to both flanking genes
• 50 to 400 nt long (avrg. gt200nt)
• higher GC content than average intergenic space
• s70 promoter
• ? independent terminator
• imperfect sequence complementary with target
  mRNA

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Characteristics of bacterial ncRNA
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The structure approach with RNAz
Function of many ncRNAs depend on a defined
secondary structure

• multiple sequence alignment
• measure of thermodynamic stability (z score)
• measure for RNA secondary structure conservation

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The structure approach
Thermodynamic stability

• calculation of the MFE (minimum free energy) as a
  measure of thermodynamic stability
• MFE depends on the length and the base
  composition of the sequence
• and is therefor difficult to interpret in
  absolute terms
• RNAz calculates a normalized measure of
  thermodynamic stability by
• compares the MFE m of a given (native) sequence
• with the MFEs of a large number of random
  sequences with similar length and base
  composition.
• A z-score is calculated as
• , where µ and s are the mean and standard
  deviations, resp., of the MFEs of the random
  samples
• negative z score indicates the a sequence is
  more stable than expected by chance

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The structure approach
Structural conservation

• RNAz predicts a consensus secondary structure for
  an alignment
• results in a consensus MFE EA
• RNAz compares this consensus MFE to the average
  MFE of the individual sequences E and calculates
  a structure conservation index
• SCI will be low if no consensus fold can be
  found.

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The structure approach

• z-score and SCI, are used to classify an
  alignment as structural RNA or other.
• RNAz uses a support vector machine (SVM) learning
  algorithm which is trained on a set of known
  ncRNAs.

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Analysis pipeline of Freiburg group
extraction of intergenic regions 50nt
BLASTN
local alignment of IGRs with BLASTN
E-value 10-8
no
discard
reverse complement
of candidate sequences
to reduce redundancy
Unify overlapping
using ClustalW
Clustering
using RNAz
Scoring
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Summary / Conclusion

• there are reliable computational methods to
  find ncRNA coding genes in bacteria
• key methods involve
• IGR extraction and filtering
• observing sequence conservation in related
  genomes (BLAST search, ClustalW alignment)
• checking for structure conservation and
  thermodynamic stability
• next step is to proof their existance
  experimentally via microArrays or Northern Blots

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Outlook

• might it be possible to predict target mRNA?

Thanks for your attention!