A short guide to long non-coding RNA gene nomenclature

The HUGO Gene Nomenclature Committee (HGNC) is the only organisation authorised to assign standardised nomenclature to human genes. Of the 38,000 approved gene symbols in our database (http://www.genenames.org), the majority represent protein-coding (pc) genes; however, we also name pseudogenes, phenotypic loci, some genomic features, and to date have named more than 8,500 human non-protein coding RNA (ncRNA) genes and ncRNA pseudogenes. We have already established unique names for most of the small ncRNA genes by working with experts for each class. Small ncRNAs can be defined into their respective classes by their shared homology and common function. In contrast, long non-coding RNA (lncRNA) genes represent a disparate set of loci related only by their size, more than 200 bases in length, share no conserved sequence homology, and have variable functions. As with pc genes, wherever possible, lncRNAs are named based on the known function of their product; a short guide is presented herein to help authors when developing novel gene symbols for lncRNAs with characterised function. Researchers must contact the HGNC with their suggestions prior to publication, to check whether the proposed gene symbol can be approved. Although thousands of lncRNAs have been predicted in the human genome, for the vast majority their function remains unresolved. lncRNA genes with no known function are named based on their genomic context. Working with lncRNA researchers, the HGNC aims to provide unique and, wherever possible, meaningful gene symbols to all lncRNA genes.     

A clonotype nomenclature for T cell receptors

T cell receptor (TCR) nucleotide sequences are often generated during analyses of T cell responses to pathogens or autoantigens. The most important region of the TCR is the third complementarity-determining region (CDR3) whose nucleotide sequence is unique to each T cell clone. The CDR3 interacts with the peptide and thus is important for recognizing pathogen or autoantigen epitopes. While conventions exist for identifying the various TCR chains, there is a lack of a concise nomenclature that would identify both the amino acid translation and nucleotide sequence of the CDR3. This deficiency makes the comparison of published TCR genetic and proteomic information difficult. To enhance information sharing among different databases and to facilitate computational assessment of clonotypic T cell repertoires, we propose a clonotype nomenclature. The rules for generating a clonotype identifier are simple and easy to follow, and have a built-in error-checking system. The identifier includes the V and J region, the CDR3 length as well as its human or mouse origin. The framework of this naming system could also be expanded to the B cell receptor. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

A short guide to technology development in cell biology

New technologies drive progress in many research fields, including cell biology. Much of technological innovation comes from “bottom-up” efforts by individual students and postdocs. However, technology development can be challenging, and a successful outcome depends on many factors. This article outlines some considerations that are important when embarking on a technology development project. Despite the challenges, developing a new technology can be extremely rewarding and could lead to a lasting impact in a given field.As is true for many fields of research, cell biology has always been propelled forward by technological innovations (Botstein, 2010). Thanks to these advances we now have access to microscopes and other equipment with exquisite resolution and sensitivity, a variety of methods to track and quantify biological molecules, and many ingenious tools to manipulate genes, molecules, organelles, and cells. In addition, we have hardware and software that enable us to analyze our data, and build models of cells and their components.Naturally, even today’s technologies have limitations, and hence there is always need for improvements and for completely novel approaches that create new opportunities. Cell biology is one of the research areas with many chances for individual young scientists to invent and develop such new technologies. Numerous recent examples illustrate that such “bottom-up” efforts can be highly successful across all areas in cell biology; e.g., as a handy vector for RNA interference (Brummelkamp et al., 2002); as methods for visualization of protein–protein or protein–DNA interactions (Roux et al., 2012; Kind et al., 2013); as tools to study chromatin (van Steensel et al., 2001), ribonucleoprotein complexes (Ule et al., 2003), or translation (Ingolia et al., 2009); or as tags for sensitive protein detection (Tanenbaum et al., 2014), just to name a few examples.As a student or postdoc, you may similarly conceive an idea for a new method or tool. Usually this idea is inspired by a biological question that you are trying to address in your ongoing research project. You might then also realize that the new method, at least on paper, may have additional applications. Yet, the development of a new technique typically requires a substantial effort. Should you halt or delay your ongoing research and embark on the development of this new technique? And if so, what is the best strategy to minimize the risks and maximize the chance of success? How do you get the most out of the investment that it takes to develop the method? Here I will discuss some issues that students and postdocs might want to consider when venturing into the development of a new technique.

To develop or not to develop

Development of a new technique can take one to five years of full-time effort, and hence can be a risky endeavor for a young scientist. The decision to start such a project therefore requires careful weighing of the pros and cons (see text box). In essence, there are four main considerations.

Points to consider before starting to develop a new technology.

•Literature search: Does a similar technology already exist? Is there published evidence for or against its feasibility?•How much time and effort will it take?•What is the chance of success?•Are you in the right environment to develop the technology?•Are simple assays available for testing and optimization?•How important are the biological questions that can be addressed?•How broadly applicable will the technology be?•What are the advantages compared with existing methods?•Is the timing right (will there be substantial interest in the technology)?•Is there potential for future applications/modifications that will further enhance the technology?•How easy will it be for other researchers to use the technology?First, conduct a thorough literature survey to ensure that the method has not been developed by others already, and to search for indications that the method may or may not work. The second consideration is the potential impact of the new technology. Impact is often difficult to predict, but it is linked to how broadly applicable the technology will be. Will the new technology only provide an answer to your specific biological question, or will it be more widely applicable? It may be helpful to ask: how many other scientists will be interested in using the technology, or at least will profit substantially from the resulting biological data or knowledge? If the answer is “about five,” then the impact will likely be low; if the answer is “possibly hundreds,” then it will certainly be worth the investment. This potential impact must be balanced against the third consideration, which is the estimated amount of time and effort it takes to develop the technology. The fourth major consideration is: What is the chance that my technique will actually work and what is the risk of failure? There is no general answer to this question, but below I will outline strategies to reduce the risk of failure and minimize the associated loss of time and effort. For this I will consider the common phases of technology development (Fig. 1).Open in a separate windowFigure 1.Flow diagram showing the typical phases of technology development.

Quick proof-of-principle

An adage that is often heard in the biotechnology industry is “fail fast.” It is OK if a project turns out to be unsuccessful, as long as the failure becomes obvious soon after the start. This way the lost investment will be minimal. In an academic setting, it may also be good to prevent finding yourself empty-handed after years of work. As a rule of thumb, I suggest that one should aim to obtain a basic proof-of-principle within approximately four months of full-time work. If after this period there still is no indication that the method may eventually work, then it may be wise to terminate the project, because further efforts are then also likely to be too time-consuming. It is thus advisable to schedule a “continue/terminate” decision point about four months after the start of the project—and stick to it. Note that at this stage the proof-of-principle evidence may be rudimentary, but it is crucial that it is convincing enough to be a firm basis for the next step: optimization.

Optimization cycles

Obtaining the first proof-of-principle evidence is a reason to celebrate, but usually it is still a long way toward a robust, generally applicable method. Careful optimization is required, through iterations of systematic tuning of parameters and testing of the performance. This can be the most time-consuming phase of technology development. To keep the cycle time of the iterative optimizations short, it is essential that a quick, easy readout is chosen. This readout should be based on a simple assay that ideally requires no more than 1–2 d. It is important that the required equipment is readily accessible; for example, if for each iteration you have to wait for several weeks to get access to an overbooked shared FACS or sequencing machine, or if you depend on the goodwill of a distant collaborator who has many other things on his mind, then the optimization process will be slow and frustrating. If your technology consists of a lengthy protocol with multiple steps, try to optimize each step individually (separated from the rest of the protocol), and include good positive and negative controls.Remember that statistical analysis is your ally: it is a tool to distinguish probable signals from random noise and thus enables you to make rational decisions in the optimization process (did condition A really yield better results than condition B?). Assays with quantitative readouts are easier to analyze statistically and are therefore preferable.

Version 1.0: Reaping the first biological insights

During the optimization process it is helpful to define an endpoint that will result in “version 1.0” of the technology. Typically this is when the technology is ready to address its first interesting biological question. Once you have reached this point, it may be useful to temporarily refrain from further optimization of the technology, and focus on applying it to this biological question. This has two purposes. First, it subjects the technology to a real-life test that may expose some of its shortcomings, which then need to be addressed in further optimization cycles. Second, it may yield biological data that illustrates the usefulness of the technology, which may inspire other scientists to adopt the method. If you are based in a strictly technology-oriented laboratory, collaboration with a colleague who is an expert in the biological system at hand may expedite this phase and help to work out bugs in the methodology.If version 1.0 performs well in this biological test, it may be time to publish the method. For senior postdocs, this may also be a good moment to start your own laboratory. A new technology is usually a perfect basis for such a step.

Disseminating and leveraging the technology

When, upon publication, other scientists adopt your new technology, they will often implement improvements and new applications, which makes the technology attractive to yet more scientists. This snowball effect is one of the hallmarks of a high-impact technology. An extreme example is the recently developed CRISPR–Cas9 technology (Doudna and Charpentier, 2014), for which improvements and new applications are currently reported almost on a weekly basis. What can you do to get such a snowball rolling?First, it helps to publish the new technology in a widely read or Open Access journal, to present it at conferences, and to initiate collaborations in order to reach a broad group of potential users. Second, the threshold for others to use the new technology must be as low as possible. Thus, implementation of the technology must be simple, and users must have easy access to detailed protocols. A website with troubleshooting advice, answers to frequently asked questions, and (if applicable) software for download will also help. Depending on the complexity of the technology, it may be worth considering whether to organize hands-on training, perhaps in the form of a short course. This may seem like a big investment, but it can substantially contribute to the snowball effect.Third, materials and software required for the technology should be readily available. Technology transfer offices of research institutes often insist on the signing of a material transfer agreement (MTA) before materials such as plasmids can be shared. But all too often this leads to a substantial administrative burden and delays of weeks or even months. Free “no-strings-attached” sharing of reagents is often the best way to promote your technology—and scientific progress in general.

Patents and the commercial route

Before publication of the technology, you may consider protecting the intellectual property by filing a patent application. Most academic institutes do this, but often the associated costs are high and the ultimate profits uncertain, in part because it can be difficult to enforce protection of a patented technology (how do you prove that your technology was used by someone else?). That said, some technologies or associated materials may be more effectively scaled up and disseminated through a commercial route than via purely academic channels. Specific companies may have distribution infrastructure or technical expertise that is hard to match in an academic laboratory. Founding your own company may also be a way to give the technology more leverage, as it provides access to funds not available in an academic setting. In these cases, timely filing of a patent application may be essential. Note that in certain countries one cannot apply for a patent once the technology has been publicly disclosed (e.g., at a conference).

Competing technologies

Often different technologies for the same purpose are invented independently and more or less simultaneously. It is therefore quite likely that sooner or later an alternative technology emerges in the literature, or appears on the commercial market. This is sometimes referred to as “competing technology,” but in an academic setting this is somewhat of a misnomer, as solid science requires multiple independent methods to cross-validate results. Moreover, it is extremely rare that two independent technologies cover exactly the same spectrum of applications. For example, one technology may have a higher resolution, but the other may be superior in sensitivity. The sudden emergence of a competing technology can however have strategic consequences, and it is important to carefully define the advantages of your technology and focus on these strengths.

A bright future for technology development

New technologies generally consist of a new combination of available technologies, or apply newly discovered fundamental principles. Because the pool of available knowledge and tools continues to expand, the opportunities to devise and test new methods will only improve. This is further facilitated by the increasing quality of basic methods and tools to build on. Thus, there is a bright future for technology development. With a carefully designed strategy, the risks associated with such efforts can be minimized and the overall impact maximized. In the end, it is extremely gratifying to apply a “home-grown” technology to exciting biological questions, and to see other laboratories use it.     

A short guide for molecular dynamics simulations of RNA systems

As a result of important methodological advances and of the rapid growth of experimental data, the number of molecular dynamics (MD) simulations related to RNA systems has significantly increased. However, such MD simulations are not straightforward and great care has to be exerted during the setup stage in order to choose the appropriate MD package, force fields and ionic conditions. Furthermore, the choice and a correct evaluation of the main characteristics of the starting structure are primordial for the generation of informative and reliable MD trajectories since experimental structures are not void of inaccuracies and errors. The aim of this review is to provide, through numerous examples, practical guidelines for the setup of MD simulations, the choice of ionic conditions and the detection and correction of experimental inaccuracies in order to start MD simulations of nucleic acid systems under the best auspices.     

New roles for beta-arrestins in cell signaling: not just for seven-transmembrane receptors

beta-arrestins, originally discovered as molecules that bind to and desensitize the activated and phosphorylated form of the G protein-coupled beta2-adrenergic receptor (beta2-AR), have recently emerged as multifunctional adaptor/scaffold proteins that dynamically assemble a wide range of multiprotein complexes in response to stimulation of most seven-transmembrane receptors (7TMRs). These complexes mediate receptor signaling, trafficking, and degradation. Moreover, beta-arrestins are increasingly found to perform analogous functions for receptors from structurally diverse classes, including atypical 7TMRs such as frizzled and smoothened, the nicotinic cholinergic receptors, receptor tyrosine kinases, and cytokine receptors, thereby regulating a growing list of cellular processes such as chemotaxis, apoptosis, and metastasis.     

Contribution of lipid mediators to the regulation of phosphatidylcholine synthesis by angiotensin

Angiotensin stimulates a cellular mitogenic response via the AT1 receptor. We have examined the effect of angiotensin on the rate of phosphatidylcholine (PC) synthesis and have begun to dissect the pathway linking the AT1 receptor to the rate-limiting enzyme in PC synthesis, CTP: phosphocholine cytidylyltransferase (CCT), using CHO cells engineered to express the AT1a receptor. Since CCT can be directly activated by lipid mediators, we probed for their involvement in the PC synthesis response to angiotensin. Angiotensin stimulated CCT activity and PC synthesis two- to threefold after a 30-min delay. The kinetics of this stimulation most closely paralleled an increase in diacylglycerol (DAG) derived from myristic acid-enriched phospholipids. The production of arachidonic acid, phosphatidic acid, or reactive oxygen species either peaked much earlier or not at all. Moreover, manipulation of the intracellular supply of oxygen free radicals, arachidonic acid, HETEs, or phosphatidic acid (using inhibitors and/or exogenous addition) did not generate parallel effects on the rate of PC synthesis. Restricting the production of DAG by inhibition of PLCbeta with U73122 reduced both basal and angiotensin-stimulated PC synthesis. The U73122 inhibition of PC synthesis was accompanied by a similar inhibition of ERK1/2 phosphorylation. Addition of exogenous DAG stimulated basal and angiotensin-dependent PC synthesis, and partially reversed the effect of the PLC inhibitor on PC synthesis. These results do not provide support for lipid mediators as direct stimulators of CCT and PC synthesis downstream of angiotensin, but give rise to the idea that angiotensin effects might be mediated via ERK1/2.     

Determination of ligand binding affinities for endogenous seven-transmembrane receptors using fluorometric microvolume assay technology.

We have developed a fluorescence-based mix and read method for the quantitative determination of receptor-ligand binding interactions. This method was used to determine IC(50) values for peptide ligands of two endogenous seven-transmembrane receptors that are expressed in cultured human cancer cells. Substance P, neurokinin A, and galanin were labeled with Cy5 and were shown to retain their native binding affinities. The cell-associated fluorescence was quantified using a fluorometric microvolume assay technology (FMAT) scanner that was designed to perform high-throughput screening assays in multiwell plates with no wash steps. The binding of fluorescently labeled substance P and neurokinin A was tested on the human astrocytoma cell line UC11 that expresses endogenous NK(1) receptor. Galanin binding was measured on endogenous galanin type 1 receptors in the Bowes neuroblastoma cell line. IC(50) values were determined for substance P, neurokinin A, and galanin and were found to correspond well with reported values from radioligand binding determinations. To demonstrate FMAT as instrumentation for high-throughput screening, it was utilized to successfully identify individual wells in a 96-well plate in which Cy5-substance P binding in UC11 cells was competed with unlabeled substance P. In addition, we developed a two-color multiplex assay in which cells individually expressing neuropeptide Y and substance P receptors were mixed in the same well. In this assay, the fluorescent ligands substance P and neuropeptide Y bound only to their respective cell types and binding was specifically competed. Therefore, two different seven-transmembrane receptor targets can be tested in one screen to minimize reagent consumption and increase throughput.     

Nuclear transport: a guide to import receptors

After synthesis in the cytoplasm, nuclear proteins traverse the nuclear envelope as a result of the specific recognition of nuclear localization signals by import. Various approaches have now uncovered a range of proteins with at least some of the characteristics expected of import receptors. This article focuses on early steps in the nuclear import of proteins and surveys the recently identified candidate import receptors.     

Pathway-oriented profiling of lipid mediators in macrophages

Macrophages produce various kinds of lipid mediators including eicosanoids and platelet-activating factor. Since they are produced from common precursors, arachidonic acid-containing phospholipids, regulations of metabolic pathways underlie the patterning of lipid mediator production. Here, we report a pathway-oriented profiling strategy of lipid mediators by a newly developed multiplex quantification system. We profiled mouse peritoneal macrophages in different activation states. The analysis of kinetics revealed the differences in the production time course of various lipid mediators, which also differed by the macrophage types. Scatterplot matrix analysis of the inhibitor study revealed correlations of lipid mediator species. The changes of these correlations provided estimates on the effects of lipopolysaccharide priming. We also found a highly linked production of 11-hydroxyeicosatetraenoic acid and prostaglandin E2, implying the in vivo property of cyclooxygenase-mediated 11-hydroxyeicosatetraenoic acid production. The present approach will serve as a strategy for understanding the regulatory mechanism of lipid mediator production.     

A standard nomenclature for structures of the kidney

A standard nomenclature for structures of the kidney