Article written by Rosa Martínez Corral.
In less than a decade, the field of genome engineering has been revolutionised by a series of techniques that now allow to accurately, efficiently and economically modify virtually any point in the genome of any organism. It all started in the early 1990s, when a pattern of repetitive sequences was observed in the DNA of certain bacteria. It was subsequently seen that many other types of bacteria also possess these patterns and, after years of research, these random observations have led to a revolution with strong implications both for research and therapy.
These repetitive sequences were part of a bacterial genomic region now known as CRISPR, or Clustered Regularly Interspaced Short Palindromic Repeats. They consist of repetitive DNA sequences that flank DNA fragments from past viral infections. Faced with a new infection by the same virus, the viral DNA fragment generates a complementary RNA molecule. This RNA guides its associated Cas proteins towards the viral genome, and these degrade the DNA of the virus. Therefore, this is a bacterial immune system that, like ours, retains a memory of past infections.
But not only that, the CRISPR-Cas system has also proved a useful tool in the laboratory, leading to a series of powerful techniques to modify the DNA of organisms. In the most widely-used version, an RNA molecule complementary to the region of the genome to be modified is designed in such a way that it guides a Cas protein to make a cut in that specific location. The cell then repairs itself, and depending on the aim and thus how the technique is applied, a mutation can either be repaired or introduced. The first is desirable, for instance, in the case where a genetic defect exists. On the other hand, in order to understand what the function of a genomic region is, scientists often make deleterious mutations to inactivate it and see what the consequence is.
The CRISPR-Cas system is, therefore, a simple and easily scalable tool that allows specific changes to be made to multiple parts of the genome, needing little time and few resources, something unprecedented in the field of genome engineering. For example, to inactivate a gene in a model organism, it was often necessary to cause random mutations and then select those of interest, involving a long and costly process. But with the CRISPR-Cas system this is no longer necessary. It has even been used to make genetic modifications in human embryos, opening new doors to the study of human development and the therapy of inherited genetic diseases.
But there are still problems to be solved. The most important of these is the fact that the system is not perfect and it can make cuts in unintended regions of the genome. It is also necessary to improve the methods that allow the Cas protein and RNA guide to be effective only in specific tissues, particularly if it is to be used as a therapeutic tool.
Post written by Toni Hermoso, bioinformatician at the CRG.
It’s been almost a decade since the term “Open Science” first appeared in Wikipedia. The page was created by Aaron Swartz and initially redirected to the “Open Access” entry. Some years later this young activist committed suicide as a result of the pressure from the judicial charges against him after having uploaded many privative licensed articles to the Internet.
Parallel to these events, Creative Commons licenses, a set of recommendations intended to foster sharing in the digital world, became increasingly popular, and many novel publishing initiatives took advantage of them for promoting open access to scientific literature.
At the same time, more and more government agencies started to demand that the benefactors of their funding should provide their publication results openly within a certain period of time. So, if research was not published originally in an open-access journal (golden road) it should be eventually uploaded in an institutional repository (green road). Furthermore, preprints, an already common practice in Physical Sciences, started to become widespread in Biosciences after the creation of portals such as BioRxiv.
However, despite the bloom of Open-Access (OA) journals and the introduction of a more favouring legislation, there are still strong concerns regarding the future of open access in science. This is mostly due to the fact that the publishing sector is effectively controlled by very few parties, which often provide pay walled content. A reaction to this situation is evidenced by initiatives such as Sci-Hub, which is defiantly providing free-access to those restricted articles.
In any case, there is more to Open Science than Open Access. We could highlight at least two other major facets: Open Data and Open Methodology. These are the indispensable two pilars for making reproducibility in modern science actually possible. In general terms, they may be the initial and raw data (straight from machines or sensors) or the final outcomes such as chart images or spreadsheets. The recent data flood has made necessary the birth of established public open repositories (e.g. Sequence Read Archive or the European Variant Archive) so researchers could freely reuse and review existing material.
It is also a common requirement from these repositories that data must be available in an open format, so other researchers may process them with different tools or versions than the ones originally used. This latter aspect is intimately associated to Open Source, which is also essential for ensuring a reproducible methodology. As a consequence, an increasing number of journals are requiring submitters to provide both data and program code so reviewers may assess by themselves that results are those that are claimed to be.
The present challenge is how to transfer those good practices -which originated in the software engineering world and later permeated into computational sciences- to the wide scientific community, where subject systems may be far less controllable (e.g., organisms or population samples). In order to help on this, there is an increasing effort in training scientists on technologies such as control version systems (e.g. GitHub), wikis or digital lab notebooks. All these kind of systems can enable collaboration of several different parties in an open and traceable way.
Even though there are some practices in everyday scientific activity, such as peer review, that are still under experimentation within the open umbrella, hopefully we may expect that in the future more and more of the key points we commented above will be just taken for granted. At that stage we might not even need to distinguish Open Science from simply SCIENCE anymore.
Some of the highlights of the event included a children’s space, where little girls and boys aged 3 to 8 could become real scientists for a day and learn whether fly larvae have a good sense of smell (the surprising answer -for the little ones- is yes!).
For the time being, we leave you here with an interview Jonas Krebs did to some of the visitors, who were repeating after their good experience last year.
If you missed the PRBB Open Day this year, have a look at this introductory video and make sure you note down the date for next year – October 1st, 2016!