Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title><jats:p>Animals and fungi have radically distinct morphologies, yet both evolved within the same eukaryotic supergroup: Opisthokonta<jats:sup>1,2</jats:sup>. Here we reconstructed the trajectory of genetic changes that accompanied the origin of Metazoa and Fungi since the divergence of Opisthokonta with a dataset that includes four novel genomes from crucial positions in the Opisthokonta phylogeny. We show that animals arose only after the accumulation of genes functionally important for their multicellularity, a tendency that began in the pre-metazoan ancestors and later accelerated in the metazoan root. By contrast, the pre-fungal ancestors experienced net losses of most functional categories, including those gained in the path to Metazoa. On a broad-scale functional level, fungal genomes contain a higher proportion of metabolic genes and diverged less from the last common ancestor of Opisthokonta than did the gene repertoires of Metazoa. Metazoa and Fungi also show differences regarding gene gain mechanisms. Gene fusions are more prevalent in Metazoa, whereas a larger fraction of gene gains were detected as horizontal gene transfers in Fungi and protists, in agreement with the long-standing idea that transfers would be less relevant in Metazoa due to germline isolation<jats:sup>3–5</jats:sup>. Together, our results indicate that animals and fungi evolved under two contrasting trajectories of genetic change that predated the origin of both groups. The gradual establishment of two clearly differentiated genomic contexts thus set the stage for the emergence of Metazoa and Fungi.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Throughout their daily lives, animals and humans often switch between different behaviours. However, neuroscience research typically studies the brain while the animal is performing one behavioural task at a time, and little is known about how brain circuits represent switches between different behaviours. Here we tested this question using an ethological setting: two bats flew together in a long 135 m tunnel, and switched between navigation when flying alone (solo) and collision avoidance as they flew past each other (cross-over). Bats increased their echolocation click rate before each cross-over, indicating attention to the other bat<jats:sup>1–9</jats:sup>. Hippocampal CA1 neurons represented the bat’s own position when flying alone (place coding<jats:sup>10–14</jats:sup>). Notably, during cross-overs, neurons switched rapidly to jointly represent the interbat distance by self-position. This neuronal switch was very fast—as fast as 100 ms—which could be revealed owing to the very rapid natural behavioural switch. The neuronal switch correlated with the attention signal, as indexed by echolocation. Interestingly, the different place fields of the same neuron often exhibited very different tuning to interbat distance, creating a complex non-separable coding of position by distance. Theoretical analysis showed that this complex representation yields more efficient coding. Overall, our results suggest that during dynamic natural behaviour, hippocampal neurons can rapidly switch their core computation to represent the relevant behavioural variables, supporting behavioural flexibility.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Cas9 is a CRISPR-associated endonuclease capable of RNA-guided, site-specific DNA cleavage<jats:sup>1–3</jats:sup>. The programmable activity of Cas9 has been widely utilized for genome editing applications<jats:sup>4–6</jats:sup>, yet its precise mechanisms of target DNA binding and off-target discrimination remain incompletely understood. Here we report a series of cryo-electron microscopy structures of <jats:italic>Streptococcus pyogenes</jats:italic> Cas9 capturing the directional process of target DNA hybridization. In the early phase of R-loop formation, the Cas9 REC2 and REC3 domains form a positively charged cleft that accommodates the distal end of the target DNA duplex. Guide–target hybridization past the seed region induces rearrangements of the REC2 and REC3 domains and relocation of the HNH nuclease domain to assume a catalytically incompetent checkpoint conformation. Completion of the guide–target heteroduplex triggers conformational activation of the HNH nuclease domain, enabled by distortion of the guide–target heteroduplex, and complementary REC2 and REC3 domain rearrangements. Together, these results establish a structural framework for target DNA-dependent activation of Cas9 that sheds light on its conformational checkpoint mechanism and may facilitate the development of novel Cas9 variants and guide RNA designs with enhanced specificity and activity.</jats:p>

<jats:title>Abstract</jats:title><jats:p>In the hippocampus, spatial maps are formed by place cells while contextual memories are thought to be encoded as engrams<jats:sup>1–6</jats:sup>. Engrams are typically identified by expression of the immediate early gene <jats:italic>Fos</jats:italic>, but little is known about the neural activity patterns that drive, and are shaped by, Fos expression in behaving animals<jats:sup>7–10</jats:sup>. Thus, it is unclear whether Fos-expressing hippocampal neurons also encode spatial maps and whether Fos expression correlates with and affects specific features of the place code<jats:sup>11</jats:sup>. Here we measured the activity of CA1 neurons with calcium imaging while monitoring Fos induction in mice performing a hippocampus-dependent spatial learning task in virtual reality. We find that neurons with high Fos induction form ensembles of cells with highly correlated activity, exhibit reliable place fields that evenly tile the environment and have more stable tuning across days than nearby non-Fos-induced cells. Comparing neighbouring cells with and without Fos function using a sparse genetic loss-of-function approach, we find that neurons with disrupted Fos function have less reliable activity, decreased spatial selectivity and lower across-day stability. Our results demonstrate that Fos-induced cells contribute to hippocampal place codes by encoding accurate, stable and spatially uniform maps and that Fos itself has a causal role in shaping these place codes. Fos ensembles may therefore link two key aspects of hippocampal function: engrams for contextual memories and place codes that underlie cognitive maps.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Individual differences in brain functional organization track a range of traits, symptoms and behaviours<jats:sup>1–12</jats:sup>. So far, work modelling linear brain–phenotype relationships has assumed that a single such relationship generalizes across all individuals, but models do not work equally well in all participants<jats:sup>13,14</jats:sup>. A better understanding of in whom models fail and why is crucial to revealing robust, useful and unbiased brain–phenotype relationships. To this end, here we related brain activity to phenotype using predictive models—trained and tested on independent data to ensure generalizability<jats:sup>15</jats:sup>—and examined model failure. We applied this data-driven approach to a range of neurocognitive measures in a new, clinically and demographically heterogeneous dataset, with the results replicated in two independent, publicly available datasets<jats:sup>16,17</jats:sup>. Across all three datasets, we find that models reflect not unitary cognitive constructs, but rather neurocognitive scores intertwined with sociodemographic and clinical covariates; that is, models reflect stereotypical profiles, and fail when applied to individuals who defy them. Model failure is reliable, phenotype specific and generalizable across datasets. Together, these results highlight the pitfalls of a one-size-fits-all modelling approach and the effect of biased phenotypic measures<jats:sup>18–20</jats:sup> on the interpretation and utility of resulting brain–phenotype models. We present a framework to address these issues so that such models may reveal the neural circuits that underlie specific phenotypes and ultimately identify individualized neural targets for clinical intervention.</jats:p>