August 23, 2010 § Leave a comment
Apoptosis is everywhere: it formed the spaces between your fingers and toes as you developed in utero, it prevents the development of T cells that would attack the cells of your body, and it shapes the structure of your brain. Too little apoptosis can cause cancer; over-zealous apoptosis causes much of the damage in a stroke or heart attack, and contributes to the failure of organ transplants. Not surprisingly, apoptosis is extremely tightly controlled at many levels. The key event in the decision to go ahead and die is the permeabilization of the mitochondrial outer membrane (a.k.a. MOMP). Cells given an apoptotic signal may wait for quite a long time before MOMP happens, and can be rescued if the conditions around them change; but once MOMP begins, under normal circumstances, there’s no going back. Proteins released from inside the mitochondrion unleash the activity of the so-called “executioner” caspases, setting off a vicious cycle that ends with most of the contents of the cell being chewed up. In fact, MOMP itself is usually enough to kill a cell, even when the downstream caspases are inhibited.
Usually, but not quite always. Recently, it’s been noticed that MOMP alone is not always a death sentence. If the caspases are inhibited, a few cells survive. Galit Lahav pointed me to an interesting paper that shows that the cells that survive do so because they have a few intact mitochondria left, which can divide and gradually repopulate the cell (Tait et al. 2010 Resistance to caspase-independent cell death requires persistence of intact mitochondria. Dev Cell. 18 802-13. PMID: 20493813)
August 19, 2010 § Leave a comment
Having listened to Naama Geva-Zetorsky’s seminar yesterday, I felt bad that I hadn’t been advertising the wonderful resource she helped build in her time in the Alon lab. So I’ve added it under the list of “databases and tools” links (Dynamic Proteomics). What you will get if you go there is a database of localization and dynamics on 1164 different genes (at the time of writing; this is, after all a dynamic database), tagged with YFP and studied in the H1299 non-small lung cell carcinoma line. The YFP is inserted by exon tagging, and each labeled gene is therefore under its endogenous promoter. You can look at images showing protein localization, with quantitation of nucleus/cytoplasm levels, and movies showing protein dynamics after exposure to the DNA-damaging drug camptothecin. It’s a remarkable resource.
And perhaps it’s not a bad idea to say a few words about what else is under there.
BioNumbers is a project Ron Milo, Paul Jorgensen and Mike Springer started while sharing a bay in the Kirschner lab. It’s a database that collects “useful” biological numbers — how much, where, how big, how fast — with references to the literature where the number was found. Ron Milo recently published a sampling of the data, which I wrote about here.
DataRail is an open source MATLAB toolbox for managing, transforming, visualizing, and modeling data, in particular high-throughput data. It was developed in the Sorger and Lauffenburger labs, primarily by Julio Saez-Rodriguez and Arthur Goldsipe, with help from Jeremy Muhlich and Bjorn Millard. I wrote a little about what it has been used for here.
GoFigure is the Megason lab’s software platform for quantitating 4D in vivo microscopy based data in high-throughput at the level of the cell, which is being developed by Arnaud Gelas, Kishore Mosaliganti, and Lydie Souhait. There’s a snippet more about it here.
little b is an open source language for building models that allows the re-use and modification of shared parts. It also provides custom notations that make models easier to read and write. It was developed in the Gunawardena lab by Aneil Mallavarapu.
MitoCarta is an inventory of 1098 mouse genes encoding proteins with strong support of mitochondrial localization. The Mootha lab performed mass spectrometry of mitochondria isolated from fourteen tissues, assessed protein localization through large-scale GFP tagging/microscopy, and integrated these results with six other genome-scale datasets of mitochondrial localization. You can search human and mouse datasets, and view images of 131 GFP-tagged proteins with mitochondrial localization.
Rule-based modeling is a rule-based language for modeling protein interaction networks. It allows you to write general rules about how proteins interact, creating executable models of protein networks. It’s based on the kappa language, orginally written by Jérôme Feret and Jean Krivine, working with Walter Fontana.
Do you know about tools that were developed to help understand biological systems at the cell/organelle/pathway level? Send me an e-mail at becky[at]hms.harvard.edu and I’ll link it. Thanks!
August 18, 2010 § Leave a comment
A recent report in Chemistry & Biology (Subach et al 2010 Red fluorescent protein with reversibly photoswitchable absorbance for photochromic FRET. Chem Biol. 17 745-55. PMID: 20659687) describes the discovery of the first red fluorescent protein that has switchable absorbance spectra. The switch is thought to happen because the chromophore undergoes a cis–trans isomerization in response to certain wavelengths of light, in this case blue or yellow light. The switchable RFP described here changes its absorbance from an “on” state with an absorbance maximum of 567 nm to an “off” state with absorbance peaking around 440 nm. In the “off” state, the emission intensity (at 585 nm) is also dramatically reduced, possibly because the chromophore is more flexible in this state.
One reason to be interested in such fluors is that they may add new power to in vivo FRET (Förster resonance energy transfer, a.k.a. fluorescent resonance energy transfer). In a form of FRET called photochromic FRET (pcFRET), which had only previously been shown to be possible with photoswitchable dyes, you can arrange matters such that the donor fluor’s emissions overlap well with the acceptor fluor’s absorbance when the acceptor is in the “on” state, and overlap poorly with absorbance in the “off” state. You can then measure the reduction in the emission from the donor when the acceptor is turned on, and see it go up again when the acceptor is turned off. This gives you a new — possibly more accurate and sensitive — way to measure proximity between the two fluors, instead of by using the output of the acceptor. If your acceptor can switch reversibly and repeatedly, which this one can, then you can switch the acceptor on and off multiple times and get a more accurate measurement of fluorescence transfer by averaging the many readings. Even if you don’t use them for FRET, you can follow biological dynamics, or label several subcellular regions one after another, or use them for super-resolution microscopy approaches such as those based on PALM (photoactivated localization microscopy).
July 30, 2010 § Leave a comment
Your Friday treat is a movie from Sean Megason’s lab of the development of a zebrafish ear. Sean has a plan to provide a complete (“in toto”) image set describing the entire development of a vertebrate, using methods described here (Megason SG (2009). In toto imaging of embryogenesis with confocal time-lapse microscopy. Methods in molecular biology (Clifton, N.J.), 546, 317-32 PMID: 19378112). When the project is complete — which will not be tomorrow — there will be a movie recording every cell division, and every morphological rearrangement, that happens as a zebrafish egg turns into a functioning fish. And then you will be able to sit at your computer and analyze vertebrate development without needing to get so much as a single finger wet.
This is from a zebrafish embryo in which both the nucleus (green) and the membrane (red) of every cell has been fluorescently labeled. If you watch carefully, you can see individual cells divide (one green blob becomes two) and move into new positions, creating (for example) the circle of cells that then opens up into the tube of the inner ear.
July 23, 2010 § Leave a comment
Neutrophils have a special place in the study of cell motility. It seems that about one in 5 talks on cell motility starts with this classic video of neutrophil crawling, from David Rogers (Vanderbilt) circa 1950 (a YouTube version set to music, just for a little variety):
(credits for original here).
I am not saying this is a bad thing. It’s a great movie, and a wonderful way to introduce a semi-naive audience to the topic. And it’s fascinating to see the neutrophil change direction in response to the movement of the bacterium. How does the neutrophil know where the bacterium has gone?
July 16, 2010 § Leave a comment
Today’s movies were generously sent to me by Markus Covert (Stanford University). This is a sampling of a very comprehensive and impressive study of the behavior of NF-κB in single cells, just published in Nature (Tay et al. 2010. Single-cell NF-kappaB dynamics reveal digital activation and analogue information processing. Nature 466 267-71. PMID: 20581820). It’s increasingly clear that a number of signaling pathways — calcium, p53, NF-κB — use spatial and temporal dynamics to deliver complex, nuanced information on the single cell level. Trying to understand how these pathways are working, or how to manipulate them using drugs, without appreciating that fact is like trying to understand what happened at the World Cup by watching a slice of the action that starts above waist level: you might be able to see when a goal or a near miss happens, but you’d have no idea about what led up to it.
What we’re looking at here is a fluorescent fusion protein of NF-κB being transported into the nucleus in response to the addition of TNF-α, then coming out again. NF-κB is a transcription factor, so it needs to be in the nucleus to do its job; moving the pre-made protein in and out of the nuclear compartment is a quick way to switch NF-κB-induced genes on and off. NF-κB is usually sequestered in the cytoplasm by a protein called IκB. When an activating signal, such as TNF-α, comes along, a kinase, IKK, is turned on (I am sweeping a good deal of complexity under the rug here) that phosphorylates IκB, causing it to be degraded by the ubiquitin pathway. The degradation of IκB releases NF-κB, which then goes into the nucleus and turns on various genes including the one for IκB.
The NF-κB—IκB interaction is thus a negative feedback loop with a fast arm (protein degradation) and a slow arm (protein synthesis), which can lead to oscillations. (Think about tweaking the taps on your shower when the temperature isn’t right (fast) — and the water taking its sweet time to make its way up the pipe to the shower head and fall onto your alternately shivering and scalded body (slow). Oscillations, and much swearing, result). The behavior of these oscillations can determine which downstream genes get activated. Since NF-κB is a remarkably versatile and important transcription factor — it’s central to the immune response, and implicated in cancer, inflammation, autoimmune disease, learning and memory — the question of exactly how the activation of downstream genes is controlled is really quite important.
July 13, 2010 § Leave a comment
Many of you know that as a post-doc in Uri Alon’s lab, Galit Lahav caused a small revolution in our understanding of how the p53 network responds to DNA damage. By looking at single cells instead of populations, she showed that individual cells responding to the damage caused by gamma-irradiation show a series of stereotyped pulses (shown in this movie); different cells show different numbers of pulses, and as you increase the amount of damage, the number of pulses per cell increases. Now the Lahav lab has identified another previously unsuspected feature of the p53 response (Loewer A, Batchelor E, Gaglia G, Lahav G. 2010. Basal Dynamics of p53 Reveal Transcriptionally Attenuated Pulses in Cycling Cells Cell 142 89-100. PMID: 20598361). It turns out that p53 is being activated in normal growing cells all the time. Because the cell cycle of cells in culture is unsynchronized, this activation can only be seen by looking at single cells. Since p53 may be the most studied protein on the planet, discovering something completely new and unexpected about its activities isn’t an everyday event.
The story started with an experiment that was originally intended as a control, looking at unstressed cells. Unexpectedly, in these unstressed, undamaged cells they found p53 pulses that are indistinguishable in shape from the pulses seen in gamma-irradiated cells. The first clue to where these pulses come from was the observation that they’re correlated with specific stages of the cell cycle, primarily happening right after mitosis. Loewer et al. used a Cdk inhibitor to show that when the cell cycle is stopped, the pulses go away. And the pulses were also selectively stopped when the ATM/DNA-PK pathway, which monitors double-stranded DNA breaks, was inhibited. It appears that these pulses are triggered by transient DNA damage that is a routine part of the cell cycle.