Gene Expression Profiling in Werner Syndrome Closely Resembles that of Normal Aging

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Gene Expression Profiling in Werner Syndrome
Closely Resembles that of Normal Aging

• By Kasper J. Kyng, Alfred May, Steen Kolvraa,
  and
• Vilhelm A. Bohr

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Werners Syndrome (Background)

• C. W. Otto Werner (50s 60s)
• Japan (31,000,000)
• Inbred marriages
• US (11,000,000 and 201,000,000)
• Autosomal Recessive Disease
• Both copies mutated and present

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Cont.

• Defects found in DNA replication, recombination,
  repair, and transcription
• Acceleration of normal aging
• Symptoms similar to that of Hutchinson-Gilford
  Syndrome (children)
• No evidence of brain aging

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• Aging timeline
• 20s graying hair, balding, scleroderma,
  weak/squeaky voice
• 30s cataracts, atropy of skin and muscle,
  chronic ulcers diabetes, osteoporosis,
  formation of cancers
• 40s death (typically) neoplasia or
  artherosclerosis

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• The phenotype of
• a Japanese
• teenager affected
• by Werners
• Syndrome

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Gene Identified

• 1996 WRN gene
• Short arm of Chromosome 8
• Mutation occurs (4 types)
• Truncated WS protein
• 2 Premature stop codons
• 1 Deletion in splice function
• 1 Mutation disabling splice junction

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• WRN possess helicase and exonuclease activities
• RecQ helicases
• Phenotypes result from transcription defect
  (secondary consequences of gene expression)
• RNA polymerase II reduced 40-60

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Materials and Methods

• Cell Lines Cultures
• 15 primary human skin fibroblasts (normal young,
  normal old, WS)
• RNA isolation Microarray Hybridization
• DAU, NIonA, NIonH

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Cont.

• Microarray Data Analysis Significance
  Statistics
• fold difference determine number of false
  positives (categorize genes WS and normal)
• Verification of Microarray Data by RT-PCR
• Using RNA isolates
• Prove reproducibility

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Results

• Goal determine gene expression changes in WS
  and old age
• Minimize variables affecting outcome
• Growth conditions Cells grown up twice and RNA
  isolated separately 4 replicate microarray
  hybridizations CV0.16
• Individual polymorphism RNA samples pooled
  (normalize sources)

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Results Cont.

• Gene Expression Analysis
• 6.3 differentially expressed
• 91 with known function w/ similar expression
  changes in WS and normal aging

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Results Cont.

• Gene expression Changes
• Pathways affected by premature and normal aging
• DNA and RNA processing (75 downregulated)
• RNA Pol II, TFDP2, FOXM1, SMARCA1 and SMARCB1

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• Upregulation of oncogenes
• WS growth deficient correlates w/ repressed
  expression of 12 genes
• BRF2, CSF3R, INSR, IGF2
• 7 cell cycle regulation genes down regulated
• Mitotic misregulation normal aging

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Stress Response, DNA Repair, Apoptosis

• Preference for specific apoptotic pathways in WS
  and normal aging
• Negative regulators down-regulated
• Increased expression of TNF gene
• Decreased expression of gene encoding protein
  protecting against TNF and apoptosis
• Decreased expression of protein for
  mitogen-activated protein kinase signaling pathway

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Discussion

• Compares global genome expression patterns of WS
  patients with normal human aging.
• 6.3 displayed significant differences in
  expression (WS or Old vs. Young)
• 91 overlapped (phenotypes pathways)
• Identified many genes not previously known to be
  regulated during aging

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• Non-human studies no single mechanism explains
  aging not much similarity in mice and humans
• Need further studies to address the RNA pol I
  dependent transcription problem (Shiratori)
• Little known of WRN in specific gene transcription

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• The study found a high degree of similarity b/w
  transcription pattern in normal aging and WS
• Other syndromes help prove contribution of
  abberant helicases to normal aging phenotypes
• DNA and RNA pathway abberations similar in WS and
  aging
• The role of protein-protein interactions

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• WRN protein involved in transcription of genes
  upstream of aging pathways
• Extended S-phase reduced replicative potential
  (cell cycle progression)
• Down-regulation of growth-related genes
• Up-regulation of oncogenes (WS and aging)
• WS causes acceleration of normal aging
• WS can be used to study normal aging pathways
  mechanisms

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• Thank You