Heterogeneity of the human metabolic network in human tumors

Late last month, the paper Heterogeneity of tumor-induced gene expression changes in the human metabolic network was published in Nature Biotechnology. In 1924, Otto Heinrich Warburg theorized that tumor cells might not derive their energy source via the usual aerobic respiratory pathway but adopt the anaerobic pathway instead. Since then, this theory has gained much support, especially for explaining the survival of tumor cells in the dense hypoxic cores of neoplasias. Regardless however, cancer metabolism has been largely ignored -- until recently. In this paper, the group of Hu, J. et al. sought to look (at the individual and systems level) for similarities or difference in the expression profiles of metabolic genes in multiple tumor types.

In brief summary, the authors analyzed a compendium of gene expression profiles collected over the past decade from microarray studies of 22 different tumor types. They used only data from the most comprehensive microarray platform to-date (HG U133 Plus 2.0) in order to capture expression profiles from the most human genes possible. Furthermore, they used data derived only from studies which used tissue from the biopsies of primary tumors.

They reported several interesting findings including:

1. The identification of several key pathways in the glycolytic cycle which are overall upregulated or downregulated across all or most of the tumor types.
2. A significant amount of heterogeneity in gene expression patterns across tumor types
3. The rewiring of the gene expression program for several key respiratory pathways

Overall, they statistically showed clear examples of metabolic pathways/genes that clearly change expression patterns when comparing tumor to normal tissue. Of these examples, some appear to be conserved across different tumor types, some appear to be dependent on cancer type. But overall, they show that the metabolic network of human tumors do go haywire and really does warrant further attention by the cancer research community.

Relation to the Warburg effect?

The authors do not make clear how their results support or refute the claim of the Warburg effect. They show that the metabolic network changes going from a normal to a tumor tissue but how these changes, together, play out is still unclear. The goal of network biology is to put biological context onto the wiring details of a biological system but the functional understanding appears to be sparse.

One hypothesis could stem from the authors' finding that, at the individual gene level, the expression program of TCA cycle components appears to have mostly changed in colon cancers. This example might hold the clues to suggest that perhaps this pathway imbalance somehow "forces" the cell to adapt anaerobic respiration. Further biochemical studies must be undertaken to address the biological question surrounding this mechanism.

Unavoidable bias.

The authors were unable to perform paired-analyses (paired based on tumor origin in terms of the patient) at the single gene level due small sample size. Furthermore, it is unclear how heterogeneous the biopsy samples from each tissue type were and the age of the patients. These are clearly unavoidable pitfalls of the data but it emphases the need for more numbers in order to better understand what is happening. There are statistical methods to attempt to "correct" for these biases but the gold-standard is usually to correct for these biases at the experimental, data-collecting stage.

Comparison to yeast.

Interestingly, the yeast metabolic network also changes during growth, depending on the availability of glucose - shifting from aerobic respiration during the early phase of growth to anaerobic during the late phase (hence the eventual production of ethanol). Since yeast are much easier to work with in the lab, it would be interesting to compare the yeast versus human cancer phenomenons. The only simple (yet major) caveat to such comparison would be that yeast were "built" within their biology to readily switch respiration pathways. The underlying difficulty with human tumors are the multitude of various mutations that must first occur. These mutations vary widely from tumor to tumor and it is currently tough to differentiate the driver from passenger mutations. This might make it difficult to make generalized conclusions about such comparisons.

Final remarks.

The paper definitely reveals insightful findings, although it is not clear how much "WOW factor" there is to the general conclusion that the metabolic network is rewired in cancer. It definitely provides some clues for where scientists can and should focus for future work.  But, as with most computational work, much experimental work will be required to better understand the biology.

World, Hello!

As this is my first post to the blogger world, I'd like to introduce myself:

My name is Tommy. I am Ph.D graduate student studying protein interactomes (networks) at Cornell.

                                             

What does that mean? 

Well, starting from the core basics - all living organisms have DNA which sometimes encode for RNA which sometimes encode for proteins. Proteins are these molecules with all sorts of strange shapes and sizes which float around in the cell and often interacts physically with everything around them, including with other proteins! These physical interactions between proteins can lead to other interactions to happen, which can then lead to other ones, and so on so forth. The point is, eventually and somehow, "protein-protein" interactions lead to very confusing and complex cascades of events that lead to the plethora of phenotypes there are. Brave souls out there have worked for decades trying to map out these cascades (or pathways) but scientists have only begun to scratch the surface. For an example of some pretty well worked out cascades, try googling "p53 cascade" or "ras cascade". Scientists like myself believe that if we can accurately draw out ALL of these interaction pathways in a cell at any given time under any given condition, we will be able to unlock exactly the mystery of how and why a cell does what it does.

It's a small-world out there.

One of the weird things about these protein networks is that, in many ways, they are quite similar to social networks. This point was first illustrated by the Watts and Strogatz in their 1998 Nature paper "Collective dynamics of 'small-world' networks". Basically, many interaction networks seem to "organize" itself by having a few things interacting with a ton of other things, whereas most things will have their own small niche. The first time I learned about this concept, I thought about the social dynamics of high school - you have the few popular kids who seem to know everyone and everyone knows them, but most of the student body will know much less people. Furthermore, many of us have probably heard the "six-degrees of separation" theory where every person in the world is separated from each other by at most 6 steps (or 6 linearly related people). In this high school example, it is pretty easy to understand the small-world idea. But the fact that networks of inanimate objects (proteins) follow very similar patterns is just bizarre. This is how I initially got intrigued by the idea of biological networks - how and why are inanimate objects, with no canonical thinking capability, able to organize their relationships the same way that humans do?

Google map it.

Today, what would you do if you wanted to find directions to some location? Use apple maps? Of course not! You would google map it. Back to the original idea of harnessing the power of interactomes, what if we could generate "Google maps" of biological entities? I think that is one of the major goals of systems biology today, one in which I am actively involved in as a graduate student.

The experiment.

Now that you know my interest lies in how things relate to each other at a network level, I will impart one of my reasons for this blog. This is an experiment (probably a poor one but it will suffice to amuse me) where I want to discover the network of my own scientific interests. I plan on blogging about anything I find amusing/interesting related to science and my science career progress. As I am in a malleable state in my scientific development, I would imagine it difficult to force myself to be interested in and post only a very narrow range of topics. If the small-world theory holds up, the long-term of this blog should do well at capturing the size of my interest network and the "architecture" of it.

I suppose I will now begin the journey that I was already on, though this would be the first time I would be documenting it. Enjoy blogger-sphere.