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  the most outstanding publications of the year 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

  • How to be an animal? Evolution of the gene repertoire across the animal tree of life      (2020)

    Gabaldón Estevan, Toni (BSC-CNS)

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    How to be an animal? Evolution of the gene repertoire across the animal tree of life     

    How to be an animal? This was the ambitious question we had in mind when we started our project. It is well-known that many animal genes have a very old origin and that they originated before the split of animals and fungi. Then, if we share these ancient genes with fungi, what makes an animal an animal? How did animals acquired their intrincated morphology, illustrated by their brains, guts or gonads? To answer this, we decided to use a bioinformatic spyglass to carefully examine more than 230 animal genomes to shed light on how their genes had evolved. And since animals are so different, we decided to have a look at the gene repertoire of all main animal lineages, called phyla - the equivalent of playing Pokemon and catching them all. Previous studies had shown that, indeed, no single gene family characterizes the origin of animals. These studies analyzed similarities between animals and relatives from the static perspective of gene composition - what genes families are in their genomes -, but lacked the dynamics that evolutionary processes have, such as gene duplications or losses. Imagine that you want to prepare cookies. If you think about the ingredientes - flour, butter, milk, sugar and eggs - they are the same ones that you need to prepare pancakes. However, the amount of each ingredient, the order in which you add them and how - mix the butter with the milk and the sugar, then add the egg, after that add half of the flour and stir together, then slowly add more flour while homogenizing the dough - is completely different. The previous studies had discovered that the “ingredients” (or genes) to be an animal or a choanoflagellate are virtually the same. Using phylogenomics, we found that gene duplication was much higher in animals than in their relatives. We found two main waves of gene duplications in animals: one at their origin, and one at the level of phylum. Remarkably, the putative function of many of the duplicated genes at different evolutionary times was related to the neural system, highlighting the complex evolutionary plasticity of this system and potentially its convergent evolution across animals.

  • DECODING ASTROCYTES (2020)

    Galea, Elena (UAB)

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    DECODING ASTROCYTES

    Astrocytes are cells of the central nervous system (CNS). Although astrocytes are less popular than neurons, it is worth emphasizing that they are an integral part of neuronal circuits. According to the prevalent view, neurons are the CNS cells that carry the complex computations subserving coding, complex behaviors, and higher brain functions. In this perspective article we ask whether, beyond providing metabolic and homeostatic support to neurons, astrocytes are fundamental to CNS coding. If they do, specific questions are whether there are niche(s) in CNS coding that would particularly profit from astrocyte idiosyncrasies, and whether the impressive techniques and theoretical armamentarium deployed for neurons could be used—and are sufficient—to unravel possible astrocyte-based coding. Pioneering theoreticians in neuroscience argued that anatomical features provide valuable insights about how the CNS operates because ‘the nervous system is a product of evolution, not design. The computational solutions evolved by nature may be unlike those that humans would invent, if only because evolutionary changes are always made within the context of a design and architecture that already is in place’. Thus, we reason that the unique anatomical arrangement between astrocytes and neurons might be part of computational solutions refined by evolution that have made the brain a highly efficient task performing system, for the brain is capable to carry out operations at a low energy expense and with a high speed unmatched by computers. In this article we explore the possible operations performed by astrocytes to efficiently integrate information from different neurons, we explain the implications of these tasks in higher brain functions, as well an in the modeling of neural circuits with artificial intelligence, and we present a roadmap to advance knowledge of astrocytes as building blocks of neural circuits.

  •   Chemically identifying molecules through ballistic electron energy losses (2020)

    García de Abajo, Francisco Javier (ICFO)

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      Chemically identifying molecules through ballistic electron energy losses

    Infrared absorption spectroscopy is routinely used to detect minute concentrations of molecules by analyzing and studying the molecular vibrational and electronic excitations. This technique has proven to be an excellent candidate for applications in areas such as medical diagnosis and detection of hazardous substances. However, because the wavelength of the infrared light used to detect the molecules is several microns, while the sample measures only a few Angstroms, molecular vibrations are excited by light with low efficiency, thus limiting this method in spatial resolution. In this study, we report on a novel approach that can chemically identify amounts of molecules at the zeptomol level (one 10-21 part of a mole, or about 600 molecules of a substance). We propose a device that uses ballistic electrons moving within a 2D semiconductor to detect and chemically identify the molecules. Instead of using photons to interact with the molecules, we use electrons that move ballistically in the semiconductor. 

    This approach is based on injecting electrons with well-defined energies through the device and having them interact with the analyte molecules placed close to the 2D-material. The interaction produces energy losses, which are directly associated with the fingerprints of the molecules. The 2D material is basically used for this purpose because it already provides vertical confinement of the electrons without the need of a vacuum chamber to run the experiment. The energy losses produced by interactions between the incident electrons and the analyte are then resolved in energy to generate a spectrum in the IR range, which exhibits the fingerprints of the molecules.

    This theoretical study demonstrates a new approach towards the identification of molecules at the zeptomol level. Through realistic simulations, we reveal a sensitivity down to the zeptomol level within a device of ~1 μm2 footprint, which could be integrated for massive multiplexing using currently available technology.

  • Emergent quantum confinement effects in hybrid halide perovskites (2020)

    Goñi, Alejandro R. (CSIC - ICMAB)

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    Emergent quantum confinement effects in hybrid halide perovskites

    Beyond their excellent photovoltaic performance, hybrid organic–inorganic metal halide perovskites exhibit unique physical phenomena and emerging functionalities prompted by the interplay between organic and inorganic components. The discovery of intrinsic quantum confinement effects in the form of oscillations in the optical absorption of formamidinium lead triiodide (FAPbI3) thin films (see Fig. 1) is a vivid example of the surprising physical properties of these hybrid materials. The relevance of this work resides in that the discrete features can be interpreted as manifestation of intrinsic quantum confinement effects, unintentionally occurring in FAPbI3 thin films. As illustrated in the inset to Fig. 1, quantum confinement acts on the electronic band structure either by discretization of the energy spectrum due to full restriction of the movement of a particle confined in deep wells (infinite potential barrier model), or by leading to formation of mini-bands, in case the (finite) confining potential exhibits periodicity. These changes in the electronic landscape lead to peaks in the joint density of states, as probed in absorption.

    Writing in Nature ‘News & Views’, I make an important contribution to the interpretation of the observed quantum oscillations. I provide a simple but strong argument against ferroelectricity as the origin of such phenomenon. The oscillations are still apparent in the temperature range of the cubic phase of FAPbI3, for which ferroelectric order is strictly forbidden by symmetry. I also reinforce the interpretation based on phase polymorphism. I propose that a combination of strain build-up, changes in the surface energy and chemical bonding between perovskite and substrate can lead to quantum confinement by unintentional formation of inclusions of the perovskite phase surrounded by thin layers of a wide-gap, non-perovskitic phase. Understanding the mechanisms that lead to the quantum oscillations may suggest new routes for manipulating the electronic band structure of hybrid perovskites at the nanoscale to enhance optoelectronic performance by exploiting the confinement-induced discretization of the energy spectrum.

  • Let there be Glyght! (2020)

    Gorostiza Langa, Pau (IBEC)
    Rovira Virgili, Carme (UB)

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    Let there be Glyght!

    Glycine receptors (GlyRs) are essential for maintaining excitatory/inhibitory balance in neuronal circuits that control reflexes and rhythmic motor behaviors. We have developed Glyght, a GlyR ligand controlled with light. It is selective over other Cys-loop receptors, is active in vivo, and displays an allosteric mechanism of action. The photomanipulation of glycinergic neurotransmission opens new avenues to understanding inhibitory circuits in intact animals and to developing drug-based phototherapies. Last but not least, Glyght constitutes a novel molecular scaffold for glycine receptor pharmacology, and offers the opportunity to expand its limited molecular toolbox.

     

  • Building machine scientists (2020)

    Guimerà Manrique, Roger (URV)

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    Building machine scientists

    Closed-form, interpretable mathematical models have been instrumental for advancing our understanding of the world. Think, for example, of Newton's law of gravitation and how its mathematical analysis has enabled us to predict astronomical phenomena with great accuracy and, perhaps more importantly, to understand central forces in general and, ultimately, the relationship between symmetry and conservation laws. With the data revolution, we may now be in a position to uncover new mathematical models for many systems from physics to the social sciences. However, to deal with increasing amounts of data, we need "machine scientists" that are able to extract these closed-form mathematical models automatically from data.

    In a series of papers, ICREA professor Roger Guimerà and colleagues at Universitat Rovira i Virgili have developed a Bayesian machine  scientist. The Bayesian machine scientist assigns model plausibilities rigorously, and establishes its prior expectations about the models by learning from a large empirical corpus of mathematical expressions. It also explores the space of all possible closed-form mathematical models in ways that provide guarantees of eventually finding the correct one, if it exists.

    For systems for which models have been proposed before, the Bayesian machine scientist is able to uncover new models that are more plausible and more predictive than the old ones, without being more complex. The machine scientist is also able to uncover accurate, closed-form mathematical models for systems for which no closed-form model was known before. In particular, Guimerà and coworkers have applied the Bayesian machine scientist to a 90 year-old problem in turbulence. For this problem they provide closed-form solutions and, despite the fact that many partial solutions have been proposed, they find that the original approach proposed in the 1930s is the most plausible one so far, outperforming even the models proposed in the last few years.

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