VL6-Non-coding RNA

• mRNA (messenger RNA)

  • encodes proteins

• tRNA (transport RNA)

  • acts as adaptor between mRNA and AA

• rRNA (ribosomal RNA)

  • forms ribosome

others:

• snRNA (small nuclear RNA)

  • has functions in various nuclear processes

• snoRNA (small nucleolar RNA)

  • facilitates chemical modifications of RNA

• miRNA (micro RNA)

  • regulates gene expression

• siRNA (small interfering RNA)

  • silences gene expression

• lncRNA (long non-coding RNA)

  • regulates gene expression

= key regulator to gene expression

• can potentially regulate hundrets of genes

• pivotal rol in many diseases

  -> used as biomarker and therapeutic target

Origin:

• during transcription of DNA to mRNA, also production of pri-miRNA

• processed by enzyme Drosha to pre-miRNA

• transported from nucleus to cytosol

• processed by Dicer enzyme to miRNA duplex

Mode of Action:

• duplex miRNA consists of

  • mature strand

    • incorperated into RNA induced silencing complex (RISC)

      -> guides RISC to target mRNA with complementary sequences in their 3' untranslated regions (UTRs)

      -> leading to translational repression or degradation of the target mRNA

  • passenger strand

    • degraded

= vital part of imune response to e.g. viruses

• involved in maintaing DNA methylation

• requires siRNAs

• allows gene silencing in genome wide scale

  • great potential for tageted therapy

• improved method -> CRISPR Cas

= precise gene-editing technology

• usage of natural defense mechanism against viruses found in bacteria and archea

Process:

• Cas9 forms complex with gRNA

  -> guide RNA = specifically designed to match target seq

• complex bindes to target sequence

• Cas protein cuts the DNA double-strand, allowing changes in the DNA

  • e.g adding a sequence

• repair with the cell’s natural repair mechanism

= competing endogenous RNA

= group of non-coding RNAs (miRNA, lncRNA, circRNA)

• contains miRNA binding sites

• influences the expression of target mRNAs by taking miRNAs from their original targets

  -> competing for shared miRNA

ceRNA models

= visiualization of how changes in the expression of miRNA targets alter the number of unbound miRNAs and lead to observable changes in miRNA activity

= mutual information under a specific condition

• can only be computed for one triplet at a time

• p-values are combined with Fisher’s method

  -> loss of power

• random permutation of gene expression vectors needed to infer an empirical p-value

• even an fast implementaion does not allow genome wide inference

Alternative: sensitvity correlation

• significance

• combinatorial effects of miRNA

null hypothesis:

condition Z has no influence on A and B

-> scor = 0

Improvement:

• extend idea to multiple miRNAs

  • gain information among signifcant interactions

  • better p-values than meta-analyses

• more degrees of freedom

• needs to consider

  • gene-miRNA

  • gene-gene

  • miRNA-miRNA

• improved significance

= Sparse Partial correlation ON Gene Expression

-> tool for reporting significant ceRNA interactions (and other) efficiently on a genome-wide scale

Repeat analysis considering the “wrong” miRNAs

-> ceRNA networt reveals biomarkers

• use single-sample enrichment scores for studying ceRNA activity with spongeEffect

  -> takes thought of Gene Set Enrichment Analysis (GSEA) method

• spongeEffect scores show ceRNA and miRNA regulatory activity in individual samples

  -> scores are predictive of breast cancer subtypes and robust against batch effects

  -> can be used as biomarkers