Blood, 1 August 2001, Vol. 98, No. 3, pp. 503-503
Analyzing myeloid differentiation on multiple fronts
Like most intricate biologic processes, the
regulation of hematopoietic differentiation is poorly understood. It
seems clear that focusing on individual gene products or taking
advantage of individual technologies is not adequate to elucidate the
intricate regulatory networks responsible for the control of blood cell production. Rather, as shown for less-complex regulatory circuits (such
as bacterial chemotaxis), an understanding of biologic regulation requires a global approach. Such an approach begins with the
comprehensive identification of regulatory network components and
culminates with the assembly of such a molecular "parts list" into
interactive networks and circuits. The behavior of these networks often
displays non-intuitively obvious properties such as robustness or a
relative resistance to potentially deleterious perturbations. A
description of how a regulatory network evolves over time and as a
function of biologic change is also necessary as a foundation of
mechanistic understanding.
Lian and colleagues (page 513) have begun to provide such a foundation
for the process of myeloid differentiation using a well-characterized
cell line model system. In this system differentiation can be induced
in a controlled and relatively synchronous manner. Therefore, as
differentiation proceeds it is possible to measure sequential changes
in the molecular differentiation program. Wisely, the authors have
analyzed gene expression changes at the level of RNA transcripts and at
the level of proteins. In addition, 2 very different methodologies for
measuring transcript levels were employed together with sophisticated
bioinformatic analyses. To date, these studies are the only examples
where global transcript and protein level changes have been analyzed
during the progression of a differentiation program. Although the data
so far have not profoundly changed our view of myeloid differentiation,
they are a wonderful example of the kinds of global approaches that
will be necessary to approach complex biologic questions in the
postgenome era. As such, the information collected by these
investigators and provided online to the scientific community
represents a "living resource" that will ultimately be integrated
with numerous other data sets and newly emerging computational modes of analysis.
Finally, the apparently poor correlation between transcript and
protein levels highlights the importance of posttranscriptional regulatory mechanisms and suggests that a degree of caution is necessary when interpreting microarray transcript analyses as indicators of functional gene-product levels. In a particularly relevant situation, while transcript profiling may be extremely useful
in categorizing hematopoietic tumors, the selection of potential
therapeutic targets will require a combination of genomic and proteomic strategies.
Ihor R. Lemischka
Princeton University
