RNA Localization: Finding FISH in a small pond
It makes sense to organize manufacturing around infrastructure. For example, if you are building ships, it is better to build them by the sea than inland. The major form of manufacturing in a cell is protein synthesis and, like shipbuilders, cells often make proteins at the sites where they are needed (Buxbaum et al., 2015). This is possible because mobile mRNA molecules can carry genetic information from the chromosomes to other sites in the cell.
Most genes in eukaryotes reside in the nucleus, but the mRNAs must be translated into proteins outside of the nucleus in the cytosol. It is thus understandable why most researchers interested in mRNA localization study the process in eukaryotic cells rather than in bacteria (which, of course, don’t have a nucleus). Bacterial cells are also, as a rule, smaller than those of eukaryotes. In fact, most bacteria are not much bigger than the diffraction limit of light. This means that it is difficult to distinguish between different structures within a bacterium under a conventional light microscope, which is likely another reason why we know much less about RNA localization in bacteria.
Now, in eLife, Xiaowei Zhuang and colleagues at Harvard University – including Jeffrey Moffitt as first author – report that the bacterium Escherichia coli also localizes its mRNAs within its cells (Moffitt et al., 2016). The Harvard team solved the problem related to the bacterium’s small size by combining a super-resolution microscopy technique called STORM (Huang et al., 2008) with a labeling technique called FISH (short for “fluorescent in situ hybridization”). Their labeling approach relied on engineering fluorescent probes that bind to specific subsets of mRNAs. These probes revealed that, like in eukaryotes, different mRNAs in bacterial cells are organized at different locations (Figure 1). Specifically, Moffitt et al. found that mRNAs that encode proteins destined for the inner membrane of the bacterium were enriched at this membrane.
Most membrane proteins insert into membranes via hydrophobic (or ‘water-hating’) sections that sit within the oil-like interior of a membrane (Sharpe et al., 2010). However, these hydrophobic sections also make the proteins prone to aggregating in the cytosol, which is mostly water. Inserting the proteins into the membrane whilst they are being built neatly solves this problem.
A protein-RNA complex called the signal recognition particle (SRP) recognizes a hydrophobic portion of a membrane protein as it emerges from the ribosome (the cellular machine that translates mRNA molecules into proteins). The SRP then shields this part of the protein, which is referred to as a signal peptide, from the watery cytosol and brings it to the membrane. In eukaryotes, a specific part of the SRP temporarily pauses the ribosome until it reaches the membrane. The SRP in bacteria lacks this part (Halic et al., 2004), yet it can nevertheless interact with an active ribosome (Noriega et al., 2014). This suggested that the SRP might still work in a similar way in eukaryotes and bacteria.
Moffitt et al. decided to test this idea. They found that mRNAs for a synthetic protein with a signal peptide did indeed localize to the inner membrane of E. coli. However, this didn’t happen if an antibiotic called kasugumycin prevented the translation process from starting. Together, these data show that the signal peptide recruits mRNAs to the membrane.
A complex of enzymes that processes and degrades RNA also localizes to the inner membrane of E. coil (Mackie, 2013). This led Moffitt et al. to ask if membrane-localized mRNAs are less stable than their counterparts in the cytosol. The answer to this question is yes: on average, membrane-associated mRNAs have shorter lives than those in the cytosol. Inhibiting translation, which is required for localization to the membrane, counteracted this effect. Preventing the RNA-degrading enzyme complex from localizing to the membrane also protected the membrane-associated mRNAs. Further research is now required to understand the biological consequences of coupling membrane localization with enhanced breakdown of RNAs.
The Nobel prize-winning biologist Jacques Monod famously quipped, “what’s true of E. coli is true of the elephant, only more so.” Moffitt et al. have now once again proven Monod right. Both E. coli and elephants (as an example of a eukaryote) localize many mRNAs within their cells. Equipped with new and powerful techniques, it will be exciting to see what else elephants can teach us about bacteria and vice versa.
References
-
In the right place at the right time: Visualizing and understanding mRNA localizationNature Reviews Molecular Cell Biology 16:95–109.https://doi.org/10.1038/nrm3918
-
RNase E: At the interface of bacterial RNA processing and decayNature Reviews Microbiology 11:45–57.https://doi.org/10.1038/nrmicro2930
Article and author information
Author details
Publication history
Copyright
© 2016, Jan
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,542
- views
-
- 179
- downloads
-
- 2
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Structural Biology and Molecular Biophysics
Images taken by transmission electron microscopes are usually affected by lens aberrations and image defocus, among other factors. These distortions can be modeled in reciprocal space using the contrast transfer function (CTF). Accurate estimation and correction of the CTF is essential for restoring the high-resolution signal in cryogenic electron microscopy (cryoEM). Previously, we described the implementation of algorithms for this task in the cisTEM software package (Grant et al., 2018). Here we show that taking sample characteristics, such as thickness and tilt, into account can improve CTF estimation. This is particularly important when imaging cellular samples, where measurement of sample thickness and geometry derived from accurate modeling of the Thon ring pattern helps judging the quality of the sample. This improved CTF estimation has been implemented in CTFFIND5, a new version of the cisTEM program CTFFIND. We evaluated the accuracy of these estimates using images of tilted aquaporin crystals and eukaryotic cells thinned by focused ion beam milling. We estimate that with micrographs of sufficient quality CTFFIND5 can measure sample tilt with an accuracy of 3° and sample thickness with an accuracy of 5 nm.
-
- Structural Biology and Molecular Biophysics
Segmentation is a critical data processing step in many applications of cryo-electron tomography. Downstream analyses, such as subtomogram averaging, are often based on segmentation results, and are thus critically dependent on the availability of open-source software for accurate as well as high-throughput tomogram segmentation. There is a need for more user-friendly, flexible, and comprehensive segmentation software that offers an insightful overview of all steps involved in preparing automated segmentations. Here, we present Ais: a dedicated tomogram segmentation package that is geared towards both high performance and accessibility, available on GitHub. In this report, we demonstrate two common processing steps that can be greatly accelerated with Ais: particle picking for subtomogram averaging, and generating many-feature segmentations of cellular architecture based on in situ tomography data. Featuring comprehensive annotation, segmentation, and rendering functionality, as well as an open repository for trained models at aiscryoet.org, we hope that Ais will help accelerate research and dissemination of data involving cryoET.