Thursday, December 19, 2019

Therapeutic Coding and non-Coding DNA Relationships

Relationships of coding and non-coding intra-gene DNA are good cause for intense research and scientific debate. Many cellular functions of non coding DNA have been discovered in the past 30 years, but prior to that these genomic regions were mostly considered 'junk'.

Probing relationships between a genes' protein coding, cDNA and at least one non-coding DNA section of the transcript, which in our work is intron1 can yield important data about genomic features in the combination. Over the past 7 years we focused on interrogating combination relationships, across multiple transcripts to construct intra-gene DNA signatures from apparently disparate DNA elements that are known to perform vastly different biological functions, yet are proximal and often adjacent.

First we considered codon to amino acid coding may operate a little different to the classical view if reading a first and second nucleotide made the third deterministic. This method would not alter the outcome of known protein coding, but it may alter the way we consider combination relationships between nucleotide's. For a transcript, any given length of cDNA and its respective intron1 sequence could possess undiscovered intrinsic order. In a model where order was tightly honored, transcript relativity may identify cDNA sequences that caused significant change in the order at each next nucleotide step.

To investigate transcripts, from the first nucleotide we computed every length cDNA k-mer. We associated k-mer's, of every possible length with the cDNA transcripts intron1 signature. Then, for a set of multiple same gene transcripts, in nucleotide order our algorithm ordered the transcripts into a vector based on their respective cDNA-kmer:intron1-signatures. Stepping through from one k-mer to the next we observed whether next k-mer significantly changed the order of transcripts in the vector. After filtering domino effects we ranked k-mers with the most significantly changed transcript order from the previous k-mer.  

Size of  circle 'K' in the example indicates k-mer length, but we only compare same length K

In the above example, it is evident that k-mer2 vs k-mer3 was the most changed because all three transcript positions moved without a domino effect. From the vector we identify intra:inter transcript conditions in next nucleotide relationships as represented in the k-mers. 

As an example, in our work with 15 viable consensus transcripts for p53 occasionally all 15 transcripts in the vector changed positions at the next k-mer. These intra transcript k-mer relationships govern the transcripts order in the vector, but when, at the next k-mer transcript order is relaxed and positions move, particularly where the significant majority of positions move it is indicative that the intra transcript k-mer condition is relative to other transcript k-mers in the vector. The more and the further transcripts move positions in the vector the more relevant their intra transcript k-mer relationships are likely to be to gene.

This transcript comparative presents a new method for diagnosis and therapy because each new transcript, when compared to the consensus set has the capacity to disrupt order in the vector and yield k-mers that are specifically relevant to the gene. In our assay testing we were able to predict and synthesize ncRNA sequences that significantly reduced proliferation of HeLa cells. In our pre-clinical work, based on comparisons to transcripts of the TP53 consensus we will be predicting the efficacy of cell and tissue selections that educate and activate Natural Killer cells.

Pre-clinical flow chart to educate NK cells with tumor tissue/cell co-cultures and prove prediction


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