Tag Archives: Bacteria

In the right place at the right time: visualizing and understanding mRNA localization

The title of this post is also the title of a review paper that I co-authored  with Adina Buxbaum, a recently graduated PhD student from Rob Singer’s lab. The review was published last week in Nature Reviews Molecular Cell biology.

In this paper we review some of the old and new methods to visualize mRNA. These include mostly FISH and MS2-like systems, which I’ve discussed extensively in this blog. There is also a short section (“box”) on quantitative analysis tools for mRNA localization imaging.

We then discuss the current knowledge on the mechanisms of mRNA localization and how it relates to the biology in two very distinct model systems – unicellular organisms (budding yeast) and the extremely polarized neuronal cell.  We also discuss examples in other organisms from bacteria through fly to frog and mammals.

I’m biased, of course, but I think this turned out to be a balanced, comprehensive, yet not too detailed review paper that will benefit both beginners which are unfamiliar with the RNA localization field, as well as experts which are used to a single method or a single model organism.
ResearchBlogging.orgBuxbaum, A., Haimovich, G., & Singer, R. (2014). In the right place at the right time: visualizing and understanding mRNA localization Nature Reviews Molecular Cell Biology DOI: 10.1038/nrm3918

The next evolutionary step

Human have always tried to improve on nature, from domestication of plants & animals through directed evolution in the test tube and GMO and up to Craig Venter’s synthetic bacteria and the expansion of the genetic code.
Today, another step was taken towards creating completely artificial life. Continue reading

FISEB 2014 – day 3

Not a lot to say. The morning session on “life science policy & research funding in Israel” was somewhat informative, but too much policy discussion and not a lot of practical info.

Next was the oral poster session on genomic and transcriptomic regulation.  Can’t discuss it much since most is unpublished work. But most lectures were interesting. My lecture was very successful. Got a lot of interest. Am happy about it.

The last session was actually a panel of university presidents/rectors etc with us – the postdocs that were invited, as the next generation of young faculty members.

It was a very nice gesture but again, I was hoping for a more practical meeting. They did share some good advice on some subjects.

After that was the poster session. Very interesting work by Roee Amit from Technion on transcription imaging in bacteria. Not ready for publication yet but stay tuned. As usual, the lab of Yaron Shav-tal produces nice imaging on transcription. They are also developing something nice with the MS2 system which caused me to say “me wants” immediately. Again, can’t discuss till its published.

In the last post, I forgot to mention the plenary lecture of David Bartel. Quite insightful about miRNAs and poly-A tails. I should go read some of his latest papers.

FISEB 2014 – day 2

Due to crappy Wi-Fi at hotel, this entry will be short. I’ll try to expand once I get back home.

Anyway, today was very interesting.

At the “early bird” session, I heard about CyTOF. Essentially, instead of using a few fluorescent markers for FACS sorting of different cell types, they offer conjugating the tagging antibodies with rare heavy metal isotopes. they claim that these are not found in cells, so the background should be zero. They have >30 different isotopes they can use, and the detection is by mass spectrometry – so very accurate and distinct identification.

Next was a session on gene expression. I won’t go into details, particularly since much is unpublished yet, but Tzachi Pilpel’s talk was amazing. Who knew tRNA may have anything to do with cancer research?

As per usual, Orna Amster-Choder talked about RNA localization in bacteria with lovely images and great data.

Jeff Gerst from Weizmann discovered a possible new mechanism of mRNA transport in yeast, using the MS2 system in very neat ways.

The next session, called “oral poster 1”  featured short talks. The most interesting to me were about mRNA methylation and about how the DNA sequence surrounding consensus sequence for DNA binding proteins affects this binding. some nice insights.

The last session I attended was about the effect of tumor microenvironment on tumor progression and treatments. Heard some amazing stories. Hope still exist to cure cancer…

Tomorrow is my lecture. Excitement!

Those repair crews work fast!

Super-resolution microscopy can potentially allow imaging of single protein molecules. A new paper now tracks single Pol and Lig proteins in E. coli, as they repair DNA damage.

The researchers replaced the endogenous proteins with proteins tagged with a photoactivatable mCherry (PAmCherry). PAmCherry is non-fluorescent, unless activated by UV light (in this case, a 405 nm laser). By using very short pulses, they activate on average less than one molecule per cell at a time. This method is called photoactivated localization microscopy (PALM).

Using this method, they were able to follow single molecules (see this movie), count the number of Pol & Lig proteins per cell, to measure their binding rates to DNA (at optimal and DNA damage states) and importantly, to measure the timing and rates of the different steps of the base excision repair process (BER).

Looking at single repair proteins by PALM. Source: Uphoff S et al. PNAS 2013;110:8063-8068

Looking at single repair proteins by PALM. Source: Uphoff S et al. PNAS 2013;110:8063-8068

Their findings show that Pol & Lig diffuse by Brownian motion near undamaged DNA, but immobilize next to damaged DNA.  Only a small fraction of the proteins (<5%) are involved in repair under normal conditions. But upon severe damage, the excess proteins come into play, to process >1000 DNA damage sites per minute!

The single proteins fix the damage at about 2 seconds per site. That’s pretty fast!

I think that as PALM and other super-resolution methods become more accessible and prevalent, we will be able to collect amazing data on the function of single proteins and other macro-molecules in vivo.

ResearchBlogging.orgUphoff S, Reyes-Lamothe R, Garza de Leon F, Sherratt DJ, & Kapanidis AN (2013). Single-molecule DNA repair in live bacteria. Proceedings of the National Academy of Sciences of the United States of America, 110 (20), 8063-8068 PMID: 23630273

Virtual cell biology

Ever wondered how all the processes inside a living cell work together as one system, in real time?  How they all collaborate with each other, affect each other?

Well, wonder no more.

Researchers from Stanford U. and the Craig Venter institute have created a computerized, simulated model of the entire life cycle of the bacterium Micoplasma genitalium (the bacteria with smallest genome [as far as we know]).

M.g. contains only 525 genes [but how many proto-genes?], and data on its genome, transcriptome, proteome and metabolome have been recently acquired. Karr et al. combined data from >900 publications to define >1900 parameters which were related to 28 modules of sub-cellular processes. Each module was first simulated according to the best fitted model (e.g. metabolism was modeled using flux-balance analysis, whereas RNA and protein degradation were modeled as Poisson processes). The difficult part, according to them, was to combine all 28 modules into one system.

They therefore assumed 1) that on short time scales (<1 sec) each module is independent of the others and 2) by running each module in 1 sec loops, they can use the variables created by all the modules from the previous loop to calculate the variables of the ongoing loop. Looping ends when the cell completes one division.

They ran the simulation 128 times, and then validated their model against databases which were not used to construct the model. They claim that the model predictions were significantly close to experimental data.

Now here comes the interesting part – their predictions. I won’t go over all of them – you can read that in the paper. But I do want to discuss a few unexpected predictions.

The metabolism regulates the cell-cycle

The cell-cycle in their model consists of three stages: initiation, replication and cytokinesis (cell division). While running the simulations, they noticed that there is a large variability between cells in the duration of the first and second stages (~65% & 38%), but very little variability in the duration of the last stage (~4%), or in the overall duration of the cell cycle (~9%).  This was strange. Why was there such a variability of the two stages, and how did this variability disappear when looking on the whole cell-cycle?

With respect to replication initiation, they found some correlation to the initial number of a protein called DnaA that must accumulates to a certain threshold at the replication start site in order to recruit DNA polymerase.  They also found that the stochastic nature of the transcription and translation modules creates variability in the number of DnaA monomers also during the simulation, not just when the simulation starts.

About the replication itself – since this is a deterministic process as they call it, they expected to see a straightforward correlation between replication and cell cycle progression. Instead, their model suggests a two-step replication process: the first step is fast. However, as soon as the availability of free deoxy-ribonucleotide-triphosphates (dNTPs) pool decreases beyond a certain threshold, replication slows to a rate equal to the rate of dNTPs production. Hence, the duration of the replication stage in individual cells is related to the free dNTP content at the start of replication than to the dNTP content at the start of the cell cycle. The availability of dNTPs imposes a sort of balance on the duration of the cell cycle – in cels with long initiation phase, the dNTP concentration will be high once replication begins, and replication duration will be short. But, if initiation starts early, then the pool of dNTP will be depleted quickly, thus slowing the replication phase. Yet, the total time of the cell cycle will not vary a great deal among individual cells.

Discovering new role and getting kinetic data for enzymatic activities

After reviewing the behavior of the wild-type cell, they went on to do simulations of single gene mutants for all 525 genes (a total of >3000 runs!).

For each gene they could determine if the cell remains viable. The simulation predictions were ~80% accurate compared to experimental data.

They then went on to experimentally test several mutants. An interesting case was the gene lpdA. The model found its deletion inviable, which was inconsistent with previous data. They therefore tested the viability of this mutant and found the strain to grow 40% slower than the wild type. They then reasoned that another protein must perform a similar task as LpdA. Indeed, they found that another gene, nox, showed similarity to lpdA in terms of sequence and function. Correcting the model with the added new task of Nox yielded a viable simulated cell.

In several other cases, there was a discrepancy between the growth rate of viable mutants in silico and in vivo.  These mutations were in gene coding certain enzymes. They then played with the kinetic parameters of the enzyme so that the outcome in silico, in terms of cell cycle duration, was similar to the outcome in vivo. Thus, they were able to calculate in silico the kinetic parameters of enzymes.

In each of these cases, the new catalytic values are consistent with experimental data.

To summarize:

Biologists are starting to realize more and more the importance of looking at the big picture, beyond their favorite protein or pathway. Systems biology is a field that is developing very fast.

Having a whole-cell in silico model is a dream of every systems biologist, indeed, of every biologist.

The fact that such a model is imperfect allows us to find discrepancies, and from them – new biological knowledge.

This model is a huge step forward in systems biology, and it is only the beginning.

Who knows, this may be the basis for designing new organisms in the future…

ResearchBlogging.orgJonathan R. Karr1, Jayodita C. Sanghvi, Derek N. Macklin, Miriam V. Gutschow, Jared M. Jacobs, Benjamin Bolival Jr., Nacyra Assad-Garcia, John I. Glass, & Markus W. Covert (2012). A Whole-Cell Computational Model Predicts Phenotype from Genotype Cell, 150 (2), 389-401 DOI: 10.1016/j.cell.2012.05.044