Highlights

Every year, a committee of experts sits down with a tough job to do: from among all ICREA publications, they must find a handful that stand out from all the others. This is indeed a challenge. The debates are sometimes heated and always difficult but, in the end, a shortlist of 24 publications is produced. No prize is awarded, and the only additional acknowledge is the honour of being chosen and highlighted by ICREA. Each piece has something unique about it, whether it be a particularly elegant solution, the huge impact it has in the media or the sheer fascination it generates as a truly new idea. For whatever the reason, these are the best of the best and, as such, we are proud to share them here.

LIST OF SCIENTIFIC HIGHLIGHTS

Format: yyyy

  • The switch of cancer (2015)

    Eyras Jiménez, Eduardo (UPF)

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    The switch of cancer

    For many years, scientists have struggled to understand and cure cancer. The study of the genome of multiple tumors has been fundamental to detect relevant alterations in cancer. These studies have highlighted the heterogeneity of genetic alterations in patients suffering from the same type of cancer, motivating the development of individualized treatments. Tumors frequently lack known actionable alterations, making thus necessary to expand the catalogue of cancer signatures to integrate other molecular alterations.

    There is increasing evidence that alterations in the splicing regulatory program play an important role in tumor transformation. Splicing is a process by which the long RNA molecule transcribed from the gene in the genome is processed to remove segments called introns, giving rise to an RNA transcript. Alternative splicing provides a mechanism to generate multiple RNA transcripts from the same gene by eliminating introns in different ways. This process is tightly regulated and can give rise to molecules with very different functions. This dramatic change is generally called a splicing switch.

    Splicing switches can induce altered cellular states, leading to disease. Accordingly, the identification of these switches can be fundamental for prognosis and therapy. This analysis is generally hindered by the heterogeneity of tumors of the same origin from different individuals, as well as by the normal variability between individuals. We have developed a new computational method, robust to biological and technical variability, which identifies significant splicing switches across a large number of tumor samples and shows high accuracy. 

    This is the first published large-scale analysis describing the splicing alterations in 9 cancer types using RNA sequencing data for more than 4000 patient samples. In this work, we have discovered that there exist many splicing switches in patients with the same cancer type that can separate with high accuracy tumor and normal samples, and different types of cancer from each other, providing potential novel molecular targets for prognosis and therapy.

  • How did yeast double its genome? (2015)

    Gabaldón Estevan, Toni (CRG)

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    How did yeast double its genome?

    The model yeast Saccharomyces cerevisiae was the first eukaryotic organism to be sequenced. From the initial analyses it appeared that the organism seemed to have two very different versions of many of its genes, implying an ancestral duplication of the whole genome. Since then, the scientific community has accepted the theory that yeast underwent a whole genome duplication, a phenomenon that is not isolated and can also be found in other species. For instance, we know that whole genome duplications were important in the early evolution of vertebrates and that it is a very common phenomenon in plants, especially cultivated ones. However the mechanism by which this whole genome duplication occurred remained unknown, and highly debated.

    In this article Gabaldón's team shows that the appearance of duplicated genes was not caused by a simple duplication of the whole genome but rather by a hybridization of two different species. Their proposal, which is at odds with the currently most widely accepted theory in the scientific community, provides new insight into this key process during genome evolution and the origins of species.The researchers analyzed genomic data with computational tools, based on cutting-edge phylogenomic methods, and designed by the Gabaldón group, to study the family gene trees. This allowed the researchers to reconstruct gene duplications and to determine what happened in evolutionary time, making it a computational equivalent of carbon-14 dating for fossils. To their surprise, they found that the age of some duplicated genes seemed to be much greater than that predicted by the theory for the whole genome duplication event. Rather than supporting a genome duplication event at the time when yeast evolved to have twice the number of chromosomes, their data indicated that the duplicated genes had begun to diverge long before. This result suggested the possibility of hybridization between species. In this case, the genes that have been duplicated still differ from each other, so that their divergence preceded the duplication of the chromosome.

     

  • Mathematical analyses of astrocyte topology in a transgenic mouse model of Alzheimer's disease (2015)

    Galea, Elena (UAB)

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    Mathematical analyses of astrocyte topology in a transgenic mouse model of Alzheimer's disease

    We sought to identify forces shaping the interaction between astrocytes and amyloid-beta plaques by performing a spatial analysis using mathematical analyses borrowed from statistical physics, and 3D images from living transgenic mice. We discovered that astrocytes are repelled by other astrocytes and by plaques. The study is relevant for three reasons. First, our conclusions contradict the current thinking about astrocytes in Alzheimer's disease. A key issue is what causes accumulation of amyloid beta plaques, and the physiological removal thereof. Plaque removal is indeed among the most highly pursued therapeutic avenues in Alzheimer's disease. Since it is widely (and wrongly) believed that astrocytes naturally migrate to plaques and eat them up, there is a line of research aimed at potentiating this capacity with drugs. After this study, people will realize that astrocytes barely move in Alzheimer's disease. This study is going to stir debate and oblige the field to reassess astrocytes as a target in Alzheimer's disease therapeutics. Second, we have identified a previously unknown aspect of astrocyte organization: the absolute minimum allowable distance between astrocytes. This demonstrates spatial organization in astrocytes. Finally, the study fills a major void in the neurosciences today, i.e., the incapability to analyze new data in meaningful ways. The advent and widespread use of imaging technologies like 2-photon microscopy has produced a wealth of increasingly complex materials, which clearly beg for computer-based analytical approaches. But these have lagged behind. There is a serious lack of tools and theoretical elaborations to help process and interpret the mass of new information. Here, we use mathematics and computer modeling to examine with great spatial precision—around 1 micron—the interactions of astrocytes and amyloid beta plaques. Although applying mathematics to biology and medicine has a long history, this study will reinforce the notion that mathematics is all the more instrumental to understand the newly-obtained information from the living brain.

  • Random diffusivity in cell membranes and connection with receptor function  (2015)

    García Parajo, Maria F. (ICFO)
    Lewenstein, Maciej Andrzej (ICFO)

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    Random diffusivity in cell membranes and connection with receptor function 

    Fundamental biological processes are regulated by molecular transport. The quantification of molecular diffusivity has fundamental importance in studying the function of biological molecules in living cells. This is because mobility is often affected by interactions between the molecule under study and its surroundings, reporting not only on the occurrence of interactions but, more importantly, it allows inferring on its functional role for cell response. One of the most powerful experimental approaches to study the mobility of individual molecules and interactions with the environment in living cells is single particle tracking (SPT).

    Although many cellular components exhibit anomalous diffusion, only recently has this sub-diffusive motion been associated with nonergodic behavior. Nonergodic dynamics refers to the difference between the properties of a particle in time and an ensemble of particles. These findings have stimulated new questions for their implications in statistical mechanics and cell biology. Is nonergodicity a common strategy shared by living systems? Which physical mechanisms generate it? What are its implications for biological function?

    Using SPT we demonstrated that the motion of the pathogen recognition receptor DC-SIGN exhibits nonergodic subdiffusion on living-cell membranes.  Indeed, the receptor undergoes changes of diffusivity, consistent with the current view of the cell membrane as a highly dynamic and diverse environment. Our experimental data could be fully recapitulated using a simple theoretical model based on ordinary random walks that change in space in time. Importantly, by studying different receptor mutants, we further correlated receptor motion to its molecular structure, thus establishing a strong link between nonergodicity and biological function.

    These results underscore the role of disorder in cell membranes and its connection with function regulation. Because of its generality, this approach offers a framework to interpret anomalous transport in other complex media where dynamic heterogeneity might play a major role, such as those found, e.g., in soft condensed matter, geology, and ecology.

  • A new tool to Predict Metastasis of Breast Cancer to the Bone (2015)

    Gomis, Roger (IRB Barcelona)

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    A new tool to Predict Metastasis of Breast Cancer to the Bone

    Bone metastasis is the only type of metastasis that can be controlled, but not cured, by drugs. Treatment is only given once the metastasis has been identified, which is normally too late. Preliminary clinical studies indicate that the same drugs used to treat metastasis could also be used to prevent it, and identifying those patients at risk of developing bone metastasis is therefore very important.

    About one million new cases of breast cancer are diagnosed each year. Preventive treatment for bone metastasis can have unwanted side effects and comes at a high cost, making a broad administration of the drugs an unviable option, even less so considering only 15-20% of patients are likely to develop metastasis over time. In order to implement a well-designed clinical trial, it is needed to know which patients may benefit and which ones will not.

    Experiments in the Growth Control and Cancer Metastasis Laboratory at the Institute for Research in Biomedicine (IRB Barcelona) led by the ICREA Research Professor Roger Gomis, have focused on the analysis of estrogen-receptor-positive breast tumors since they specifically tend to metastasize to the bone, and represent 80% of all breast cancers. The results indicate that the gene MAF triggers a set of functions in the cell that allow metastasis to take place in the bone.

    The researchers analyzed more than 900 clinical samples of primary breast tumors. In tumors in which the MAF gene is altered, the risk of metastasis to the bone is 14 times higher than in those in which it is unaltered. Thus, the biomarker reliably predicts metastasis to the bone. Studying whether it is highly expressed in breast cancer patients to determine whether this also happens in a clinical setting is an important next step. It could improve the quality of life of these patients and the way clinicians manage their cancer. And this is exactly what we the researchers are doing in collaboration with other institutions.

  • HEAR-EU: Computational models of cochlear implants (2015)

    González Ballester, Miguel A. (UPF)

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    HEAR-EU: Computational models of cochlear implants

    A cochlear implant (also known as bionic ear) is a device that completely replaces the hearing system by implantable electronics, a remarkable step towards cyborg technology. It leads to hearing restoration in patients with moderate to severe hearing loss, by direct auditory nerve stimulation. 

    Electrical stimulation of the brain has been extremely successful to date: important examples include cochlear implants and deep brain stimulation (DBS), which are two extremely effective and safe techniques. However, in both cases, the extent of the electrical stimulation and its effect on the brain is not well known. This, combined with the lack of pre-operative measures that predict the outcomes after implantation, results in high variability in the patient’s response. We argue that this variability could be reduced by the use of predictive computational models.

    The aim of the HEAR-EU project (FP7 project coordinated by ICREA Professor Miguel A. González Ballester) is to develop computational tools to improve the prediction of cochlear implantation surgery and, consequently, to enable the improvement of hearing implant designs. As part of this project, in this work we present a framework that combines the use of highly detailed imaging techniques, finite element methods, flexible CAD structures, and a dynamic model of the nerve fibers, thus improving our knowledge of the nervous system–electrode interface.

    We apply our framework to the case of cochlear implants, showing how we can predict nerve response for patients with both intact and degenerated nerve fibers. Then, using the predicted response, we calculate a metric for the usefulness of the stimulation protocol and use this information to rerun the simulations with better parameters, thus leading to optimized patient-specific treatment.

    This work was carried out in collaboration with NASA Ames Research Center in California.

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