Cancer Initiation: Description of the "Alcorn Prize" in computational biology

Wednesday, September 4, 2013

Description of the "Alcorn Prize" in computational biology

Molecular biology is undergoing a "paradigm shift" in the way it views and researches pathological medical conditions. The overall catchword in the shift is "epigenetics". In what we call the traditional view, or central dogma, as Francis Crick described it, describes all biological systems as having information encoded in DNA, transcribed to RNA, and translated to sequences of amino acids known as proteins. This view has held up remarkably well as an organizing foundation for research for quite a few decades.  A traditional research project in medical genomics assumes that any anomalies in DNA sequence will be mechanically transcribed to RNA and then on to protein. Thus, it is assumed that malfunctioning genes result from malformed proteins. This view works well in laboratory model systems. More recently, primarily within the last decade, it has been realized that information flows in the cell are quite a bit more sophisticated.
   An example is the well known "guardian of the genome" known as p53.  As one of the earlier genes found to be highly associated with cancer, there have been literally thousands of papers and studies analyzing the myriad of cellular functions associated with p53. More recently it has been found that in many cases, if not most cases, p53 expression is lost in (ovarian) cancer. What this abstract describes in a pattern where the promoter of the gene has become methylated by epigenetic processes, and as a result, there is no p53 being expressed at all, regardless of anomalies to the genomic sequence. In other words, there is only marginal utility to doing further laboratory research on the p53 protein, as in most as many as half the cases reported ( if this study is representative), there is no p53 actually in the cell, itself a serious problem. A similar situation exists for the most of the usual culprits in cancer research, pRB ( retinoblastoma ), BRCA1/2, and telomerase. A slight variation in the case of telomerase in that this is a gene that is thought to be best unexpressed in somatic cells as a defense against runaway proliferation known as the Hayflick limit.
   Regulation of a genomic transcript at the RNA level is known to be a function of a particular RNA's secondary structure.  RNA secondary structure, for large transcripts is not a fixed description but a probabilistic description in that the actual shape of a transcript changes continuously as a result of its size, Brownian motion, and interaction with other transcripts ( regulatory micro RNAs , miRNA ).
   How then should can we go about prediction structural changes associated with genomic anomalies, such as those seen in BRCA1/2? This is the unsolved problem which faces the entire research community, it that prediction of secondary structure for large transcripts has proven unsuccessful.
   With one small exception.
   In 2009 Alcorn et. al. reported that they had designed a ribozyme which differentially targets wild type (WT) and mutant (MT) transcripts. The excerpt below from the "Hammerhead ribozyme vectors" section of the methods clearly states that the investigators used S-fold to model the the predicted secondary structure of the normal ( WT ) and mutant ( MT ) transcripts. They discovered that each of the mutations associated with pseudoachondroplasia was also associated with a flip in the secondary structure of the mRNA. As such, they could design a ribozyme, or any antisense based tool to selectively targeted the MT transcript and had reduced activity associated with the WT transcript.

Excerpted from Alcorn:


 S-fold was used to compare the predicted secondary structures for normal and three mutant COMP sequences chosen for this study, all of which affected the predicted folding pattern of the target transcripts in a manner that could impact interactions with targeted ribozymes. Ribozyme 56 cuts at nucleotide 56 of the COMP mRNA: 5′-CUGCCCUCGGCGCGUCCGGACAGGGCCAG-3′. The cut site (underlined) lies in the region of the transcript encoding the C-terminal half of the signal sequence extending just into the coding sequence for the mature protein.

 It is known that interaction between a target transcript and it's antisense partner is dependent upon the degree of self-binding ( cis ) within the target. Each biologically relavant mRNA consists of a series of stems and loops, where the stems are double stranded RNA and the loops are single stranded RNA. In this case, the investigators chose a cut site (GUC) just upstream of the start codon.
  It should be noted that in terms of epigenetic regulation of expressed proteins, the fact that all three mutants were associated with structural modifications in the mRNA of COMP is in itself suspicious. This observation, associated only with computational preliminaries to the investigation should have opened the door epigenetic considerations in the investigation.

In the discussion section of Alcorn, the author discusses the significance of his discovery:

Most importantly, the ribozyme is more active in knockdown of three well-described MT-COMP species in chondrocytes when compared with normal WT-COMP (Fig. 4). Such preference presumably occurs because of differences in the secondary structure () or accessibility of target sequences in ribonucleoprotein complexes () of the transcripts encoding wild-type versus mutant COMP. Selective targeting of mutant COMP over WT-COMP by a ribozyme has never been reported.
  

   Thus, Alcorn is stating that he predicted the change in secondary structure is somewhat unique in that the transcript for COMP weighs in at 2471 base pairs. This is well beyond what is currently considered within todays current computational technology.
 
  Thus the  Alcorn Prize is completely defined. It is the team or person which develops a computational biology system that can replicate Alcorns achieviement for COMP for broadly relavant transcripts such as p53, retinoblastoma (pRB )  Breast Cancer ( BRCA1/2) and polycystin. Good luck to all. Ready GO!

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