Biology Reference
In-Depth Information
The RNA trajectories measured by Garcia-Martinez et al. (2004) provide indi-
rect experimental evidence that structural genes can contribute to regulating the
intracellular levels of their own transcripts. This novel idea is presented below.
carries two kinds of information - (1) the name of the gene (or the open reading
frame, ORF) encoding the RNA molecule whose concentration is being measured,
and (2) the time-dependent change in the intracellular concentration of the RNA
(i.e., the ribons). The former can be represented in the N-dimensional sequence
(or
genotype
) space, where a point represents an N nucleotide-long RNA molecule,
and the latter in the six-dimensional concentration (
phenotype
) space, wherein a
point denotes the ribon or the kinetic trajectory of an RNA molecule measured over
the six time points. Thus, for any pair of RNA molecules, it is possible to calculate
(1) the
genotypic similarity
as the degree of the overlap between the pair of nucleo-
tide sequences (using the ClustalW2 program on line (Chenna et al. 2003)), and
(2) the
phenotypic distance
as the Euclidean distance between the corresponding two
points in the six-dimensional concentration space. When the phenotypic distances of
a set of all possible RNA pairs (numbering n(n
1)/2 where n is the number of RNA
molecules belonging to a given metabolic pathway such as glycolysis and oxidative
phosphorylation) were plotted against the associated genotypic similarities, the
results shown in Fig.
12.17
were obtained. To facilitate comparisons, several
functional groups of RNA molecules are plotted in one graph in Fig.
12.18
.
One of the most unexpected observations to be made in these plots is that most,
if not all, of the points belonging to a given function or metabolic pathway lie below
a line with a characteristic negative slope (see Table
12.7
). We will refer to this
phenomenon as the “
triangular distribution of the genotype similarity
vs
. phenotype
distance (GSvPD) plots
.” This triangular distribution indicates that structural genes
have an effect on the intracellular levels of their own transcripts (but it is impossible
to predict the phenotype based on genotype, see below), because, if
structural genes
had no effect at all on their transcript levels inside the cell (as currently widely
believed by most molecular biologists), the distribution of the points on the GSvPD
plot should be random and hence cannot account for the triangular distributions
observed. On the other hand, if structural genes had a complete control over their
intracellular transcript levels, all the points should lie along the diagonal line,
but only a very small fraction of the points actually lie close to it. More than 95%
of the points in Figs.
12.17
and
12.18
are contained in the region below the diagonal
line. So the relation between genotype and phenotype as revealed in the GSvPD
plots contains some regularities but these regularities are unpredictable, leading
to the conclusion that the genotype-phenotype relation is
stochastic
or
quasi-
deterministic
(see
Glossary
) (Ji et al. 2009b).
A greater absolute slope in the GSvPD plot indicates a greater variation in
(or smaller control on) phenotypes for a given genotypic variation (see Figs.
12.17
,
12.18
). Thus, a greater absolute slope of a GSvPD plot can be interpreted as an
indication of a smaller effect of structural genes on the intracellular concentrations
of their transcripts, leading to the suggestion that the inverse of the absolute value
