The mysteries of blood

Be honest — would you have guessed that red blood cells are mysterious?  No, I wouldn’t have either.  They’re the simplest cells in our bodies, for goodness sake — they don’t even have DNA.  All they do is carry hemoglobin around, picking up oxygen as they pass the lungs and gradually dumping it everywhere else.  How hard can that be to understand?  And we’ve studied them in various ways for over 450 years.

But indeed it turns out that there are significant holes in our knowledge of how the number, size and hemoglobin concentrations of red blood cells are controlled, and how these control systems go wrong in anemia.  We do know where new red blood cells come from — the bone marrow — and we know some of the factors that control the development and release of new red blood cells, such as erythropoietin.  The feedback control between “too few red cells” and “more erythropoietin needed” goes mainly through the kidneys; the mechanism the kidney uses to sense oxygen levels (protein hydroxylation) and induce erythropoietin synthesis has been an area of active research.  What we know less about is what happens to these new red blood cells once they get out in the circulation.

Peter Sorger’s Stetten Lecture

Peter Sorger recently gave a talk at NIH entitled “Measuring and Modeling Life-Death Decisions in Single Cells” as part of the prestigious Stetten Lecture series. NIGMS sponsors the Stetten Lectures to honor Dr. DeWitt Stetten, Jr., who directed the Institute from 1970 to 1974 and had a strong commitment to basic research. Since NIH kindly put the video on line, I thought you might be interested to see it.

Here’s the lecture summary:

When monitoring signal transduction at the level of single living cells, we observe remarkably complex dynamics and great variability from one cell to the next. However, it is also clear that finely tuned interactions among processes operating on quite different time scales are essential in cell fate determination and, conversely, that errors in coordination underlie many oncogenic changes. How can the observed variability among genetically identical cells be reconciled with an apparent requirement for precise control, what is the impact of variability on the evolution of tumors and what are the implications for emergence of drug resistant cancers in patients?

Equal rights for (stem cell) daughters

I’ve been thinking about writing about these papers for a while, but Nature just beat me to it.*  (I am definitely behind on my posting!)  A smidgen of information to encourage you to read more: when people talk about how stem cells manage to divide indefinitely, they usually argue that there is some kind of asymmetric division in which the stem-cell-ness of the stem cell stays with one daughter, which can then continue to divide in perpetuity.  The other daughter is marked for differentiation and (eventual) death.  Two experimental groups (Douglas  Winton‘s lab and Hans Clevers‘ lab) collaborated with a team of  theorists (Allon Klein and Ben Simons) and came up with what looks like strong evidence that, in the context of intestinal stem cells, this cannot be true.  Instead, it appears both daughters have equal stem cell potential; which has significant consequences for the way we thought stem cell numbers were controlled.  Take a look.  I think you’ll enjoy it.

* Oh.  And so did Developmental Cell.

Theory Lunch this week

Ergodic Rate Analysis (ERA): a new method for retrieving rates and regulation of signaling events from analysis of fixed cells

Friday, 22 October 2010, 12pm
Warren Alpert 563 – HMS
Ran Kafri
Kirschner and Lahav Labs, Department of Systems Biology
Harvard Medical School
Jason Levy
Department of Mathematics
University of Ottawa

Abstract
An inherent flaw in mathematical models of signal transduction is that they typically consist of differential equations that describe our pre-existing narratives of the signaling events, making it hard to “think outside the box”. In practice, there are always numerous different possible models (narratives) that fit any given set of measurements. Typically, such models would also consist of more free parameters than measurements can actually support.

Confronting these limitations, we have developed Ergodic Rate Analysis (ERA), a method of deriving protein dynamics through basic conservation principles. From individual measurements of large populations of fixed cells, ERA retrieves both dynamics and positive/negative feedback, without requiring prior assumptions or free parameters. Direct measurements using time lapse microscopy have confirmed ERA predictions. We will describe the results of ERA on regulation of protein mass production in the cell cycle of cancer cells.

Theory Lunch schedule here.

Pizza Talk reminder

Tomorrow, October 19th, Warren Alpert 563, 12.30 pm

Ran Kafri

“Dynamics and Negative Feedbacks in the Regulation of Protein Mass Production — A Demonstration of Ergodic Rate Analysis (ERA)”

Pizza talk schedule here.

… don’t miss your pizza…

Theory Lunch this week

Network instabilities and cancer

Friday, 15 Oct 2010, 12pm
Warren Alpert 563 – HMS

Baltazar Aguda
Neuro-Oncology Branch
National Cancer Institute, NIH

Abstract
I would like to share the story of how our group used qualitative network analysis (qNET) to home in on the essential feedback loops in the G1-S transition of the mammalian cell cycle for generating the switching behavior associated with the Restriction Point. This important cell cycle checkpoint is dysregulated in many human cancers. The qNET analysis focuses on the topology of the interaction network and the identification of destabilizing cycles that potentially cause instability and switching dynamics. The regulation of the R point by microRNAs and its coordination with apoptosis will also be briefly discussed in relation to qNET analysis.

References
1.    B D Aguda, A B Goryachev, “From pathways databases to network models of switching behavior”, PLoS Comp Biol 3:1674-8N 2007. PubMed
2.    B D Aguda, Y Kim, M G Piper-Hunter, A Friedman, C B Marsh, “MicroRNA regulation of a cancer network: consequences of the feedback loops involving miR-17-92, E2F, and Myc”, PNAS 105:19678-83 2008. PubMed
3.    B D Aguda, C K Algar, “A structural analysis of the qualitative networks regulating the cell cycle and apoptosis”, Cell Cycle 2:538-44B 2003. PubMed

Theory Lunch schedule here.

Repost: The ancestor of the hairball

While we were playing “cite the oldest paper“, Pam Silver suggested this paper (Srb, AM and Horowitz, NH, 1944. The ornithine cycle in Neurospora and its genetic control.  J. Biol. Chem 154 129-139), as a distant antecedent of the field we now call systems biology. Published only three years after Beadle and Tatum used Neurospora to demonstrate the connection between genes and enzymes, and at a time when the nature of genes was uncertain — it was suspected that they consisted of nucleoprotein complexes, or at least contained such complexes as essential elements — this paper describes the existence of a network of genes whose products perform a complex set of biochemical reactions, producing arginine.

Now one of the less fashionable model organisms, but once a supremely important one, Neurospora first came to scientific notice as a major pest in bakeries, growing as large salmon-colored clouds on the bread loaves. The menace was later reduced somewhat by the routine use of mold inhibitors, but by that time scientists were hooked.  In the ’20s it was discovered that what makes Neurospora so good at thriving in bakeries is that the spores survive high heat (indeed, require heat to germinate).  It also turns out to be the first organism that colonizes areas that have been semi-sterilized by volcanic eruptions — “producing great masses of brilliant conidia of bizarre appearance”.  [Google Images has failed me on this one, if anyone can find some “brilliant conidia” please send me a picture or a link]. Early scientists who collected Neurospora species in the tropics ran in to some difficulty: it had a tendency to grow straight through the cotton wool they were using to close the tops of their tubes. But once they got it into a cool, dry climate, it became more manageable.

Theory Lunch this week

How does the first cell in a tumor behave?

Friday, 8 Oct 2010, 12pm
Warren Alpert 563 – HMS

Allon Klein
Cavendish Laboratory of Physics, Cambridge, and
Department of Systems Biology, HMS

Abstract
UV B (UVB) radiation induces mutations to the p53 tumor suppressor gene, leading to skin cancer. How do the earliest p53 mutant cells behave? Do these cells already disrupt homeostasis? In this talk I will discuss how clone size statistics from mice and humans exposed to UVB radiation discloses the growth dynamics of p53 mutant clones [1]. The data show that p53 mutation leads to excess cell proliferation very early on; but ending UVB exposure rebalances p53 mutant behaviour. This has implications for deciding how to plan your time on the beach: ongoing low-intensity UVB radiation increases the number of precancerous cells dramatically compared with sporadic, higher-intensity exposure at the same cumulative dose. Repeating these experiments with several transgenic mice provides hints of the mechanism underlying loss of homeostasis in p53 mutant cells.

References
1.    A M Klein, D E Brash, P H Jones, B D Simons, “Stochastic fate of p53-mutant epidermal progenitor cells is tilted toward proliferation by UV B during preneoplasia”, PNAS 107:270-275 2010. PubMed

Theory Lunch schedule

The long-awaited return of Theory Lunch

This Friday, Oct 1: Warren Alpert 563, 12 noon.  Hooray!  See you there.

The schedule for the rest of the year is here.

Quantitative issues in designing anti-cancer protein drugs
Jeffrey Way
Advanced Technology Team
Wyss Institute, Harvard

Abstract
The problem of delivering proteins to specific target cells is particularly acute for treating solid tumors because, unlike other tissues, tumors have no lymph node drainage. Thus, large (and small) molecules enter tumors by diffusion and penetration is almost always poor. As a result, action of drugs on normal tissues can predominate, leading to unacceptable side effects. To address this problem, we have taken a quantitative approach to minimizing the action of protein drugs on non-target tissues.

Evolution modulates the quantitative characteristics of protein interactions and often uses combinations of weak interactions to achieve a particular specificity. We addressed how quantitative optimization might be used in the design of multidomain proteins, constructing fusion proteins between EGF and interferon-alpha (IFNa), the anti-tumor antibody MR1-1 and IFNa, and the anti-glycophorin antibody 10F7 and erythropoietin (Epo). The goal was to generate proteins in which the cell binding of an “activity element” (IFNa or Epo) was driven by prior binding of a “targeting element”. We connected these elements with a linker that allows each fusion partner to simultaneously bind their receptors on a cell surface, and incorporated mutations into the IFNa and Epo elements that progressively decrease the binding of these ligands to their receptors. When the activity element is appropriately mutated, the selectivity of the resulting fusion protein can be enhanced at least 10- to 20-fold compared to a fusion protein consisting of only wild-type elements. These results support a quantitative approach in the design of fusion proteins.

References

• N D Taylor, J C Way, P A Silver, P Cironi, “Anti-glycophorin single-chain Fv fusion to low-affinity mutant erythropoietin improves red blood cell-lineage specificity”, Protein Eng Des Sel 23:251-60 2010. PubMed
• P Cironi, I A Swinburne, P A Silver, “Enhancement of cell type specificity by quantitative modulation of a chimeric ligand”, J Biol Chem 283:8469-76 2008. PubMed

Harvard tenure-track position in systems biology

It’s that time of year… here’s an ad from our friends and colleagues at the Department of Molecular and Cellular Biology and FAS Center for Systems Biology, on Harvard University’s main campus.  This is a joint search between the MCB Department and the FAS Center.