Caroline C. Escoubas, Leah C. Dorman, Phi T. Nguyen, Christian Lagares-Linares, Haruna Nakajo, Sarah R. Anderson, Beatriz Cuevas, Ilia D. Vainchtein, Nicholas J. Silva, Yinghong Xiao, Peter V. Lidsky, Ellen Y. Wang, Sunrae E. Taloma, Hiromi Nakao-Inoue, Bjoern Schwer, Raul Andino, Tomasz J. Nowakowski, Anna V. Molofsky <<<

SummaryMicroglia are brain resident phagocytes that can engulf synaptic components and extracellular matrix as well as whole neurons. However, whether there are unique molecular mechanisms that regulate these distinct phagocytic states is unknown. Here we define a molecularly distinct microglial subset whose function is to engulf neurons in the developing brain. We transcriptomically identified a cluster of Type I interferon (IFN-I) responsive microglia that expanded 20-fold in the postnatal day 5 somatosensory cortex after partial whisker deprivation, a stressor that accelerates neural circuit remodeling.In situ, IFN-I responsive microglia were highly phagocytic and actively engulfed whole neurons. Conditional deletion of IFN-I signaling (Ifnar1fl/fl) in microglia but not neurons resulted in dysmorphic microglia with stalled phagocytosis and an accumulation of neurons with double strand DNA breaks, a marker of cell stress. Conversely, exogenous IFN-I was sufficient to drive neuronal engulfment by microglia and restrict the accumulation of damaged neurons. IFN-I deficient mice had excess excitatory neurons in the developing somatosensory cortex as well as tactile hypersensitivity to whisker stimulation. These data define a molecular mechanism through which microglia engulf neurons during a critical window of brain development. More broadly, they reveal key homeostatic roles of a canonical antiviral signaling pathway in brain development. <<<

AbstractUnderstanding the structure of a problem, such as the relationships between stimuli, supports fast learning and flexible reasoning. Recent theoretical suggestions have highlighted the usefulness of explicit structural representations that are fully divorced from sensory details for generalisation. Rodent work has suggested that abstraction of structure occurs gradually, over time, in cortex. However, direct evidence of such explicit relational representations in humans is scarce, and its relationship to consolidation mechanisms is underexplored. Here, we use a graph-learning paradigm to find such a relational map in the human medial prefrontal cortex. Importantly, this representation was absent early after learning but emerged on the time scale of days. These results shed new light on neural representations underlying the remarkable human ability to draw accurate inferences from little data. <<<

AbstractThe burgeoning interest in in situ multiplexed gene expression profiling technologies has opened new avenues for understanding cellular behavior and interactions. In this study, we present a comparative benchmark analysis of six in situ gene expression profiling methods, including both commercially available and academically developed methods, using publicly accessible mouse brain datasets. We find that standard sensitivity metrics, such as the number of unique molecules detected per cell, are not directly comparable across datasets due to substantial differences in the incidence of off-target molecular artifacts impacting specificity. To address these challenges, we explored various potential sources of molecular artifacts, developed novel metrics to control for them, and utilized these metrics to evaluate and compare different in situ technologies. Finally, we demonstrate how molecular false positives can seriously confound spatially-aware differential expression analysis, requiring caution in the interpretation of downstream results. Our analysis provides guidance for the selection, processing, and interpretation of in situ spatial technologies. <<<

AbstractThe hippocampal-entorhinal system uses cognitive maps to represent spatial knowledge and other types of relational information, such as the transition probabilities between objects. However, objects can often be characterized in terms of different types of relations simultaneously, e.g. semantic similarities learned over the course of a lifetime as well as transitions experienced over a brief timeframe in an experimental setting. Here we ask how the hippocampal formation handles the embedding of stimuli in multiple relational structures that differ vastly in terms of their mode and timescale of acquisition: Does it integrate the different stimulus dimensions into one conjunctive map, or is each dimension represented in a parallel map? To this end, we reanalyzed functional magnetic resonance imaging (fMRI) data from Garvert et al. (2017) that had previously revealed an entorhinal map which coded for newly learnt statistical regularities. We used a triplet odd-one-out task to construct a semantic distance matrix for presented items and applied fMRI adaptation analysis to show that the degree of similarity of representations in bilateral hippocampus decreases as a function of semantic distance between presented objects. Importantly, while both maps localize to the hippocampal formation, this semantic map is anatomically distinct from the originally described entorhinal map. This finding supports the idea that the hippocampal-entorhinal system forms parallel cognitive maps reflecting the embedding of objects in diverse relational structures. <<<

Ivan Anishchenko, Yakov Kipnis, Indrek Kalvet, Guangfeng Zhou, Rohith Krishna, Samuel J. Pellock, Anna Lauko, Gyu Rie Lee, Linna An, Justas Dauparas, Frank DiMaio, David Baker <<<

AbstractModeling the conformational heterogeneity of protein-small molecule systems is an outstanding challenge. We reasoned that while residue level descriptions of biomolecules are efficient for de novo structure prediction, for probing heterogeneity of interactions with small molecules in the folded state an entirely atomic level description could have advantages in speed and generality. We developed a graph neural network called ChemNet trained to recapitulate correct atomic positions from partially corrupted input structures from the Cambridge Structural Database and the Protein Data Bank; the nodes of the graph are the atoms in the system. ChemNet accurately generates structures of diverse organic small molecules given knowledge of their atom composition and bonding, and given a description of the larger protein context, and builds up structures of small molecules and protein side chains for protein-small molecule docking. Because ChemNet is rapid and stochastic, ensembles of predictions can be readily generated to map conformational heterogeneity. In enzyme design efforts described here and elsewhere, we find that using ChemNet to assess the accuracy and pre-organization of the designed active sites results in higher success rates and higher activities; we obtain a preorganized retroaldolase with akcat/KMof 11000 M-1min- 1, considerably higher than any pre-deep learning design for this reaction. We anticipate that ChemNet will be widely useful for rapidly generating conformational ensembles of small molecule and small molecule-protein systems, and for designing higher activity preorganized enzymes. <<<

AbstractSeveral computational methods have recently been developed for characterizing molecular tissue regions in spatially resolved transcriptomics (SRT) data. However, each method fundamentally relies on spatially smoothing transcriptomic features across neighboring cells. Here, we demonstrate that smoothing increases autocorrelation between neighboring cells, causing latent space to encode physical adjacency rather than spatial transcriptomic patterns. We find that randomly sub-sampling neighbors before smoothing mitigates autocorrelation, improving the performance of existing methods and further enabling a simpler, more efficient approach that we callspatialintegration (SPIN). SPIN leverages the conventional single-cell toolkit, yielding spatial analogies to each tool: clustering identifies molecular tissue regions; differentially expressed gene analysis calculates region marker genes; trajectory inference reveals continuous, molecularly defined ana tomical axes; and integration allows joint analysis across multiple SRT datasets, regardless of tissue morphology, spatial resolution, or experimental technology. We apply SPIN to SRT datasets from mouse and marmoset brains to calculate shared and species-specific region marker genes as well as a molecularly defined neocortical depth axis along which several genes and cell types differ across species. <<<

AbstractThe rational targeting of RNA with small molecules is hampered by our still limited understanding of RNA structural and dynamic properties. Mostin silicotools for binding site identification rely on static structures and therefore cannot face the challenges posed by the dynamic nature of RNA molecules. Here, we present SHAMAN, a computational technique to identify potential small-molecule binding sites in RNA structural ensembles. SHAMAN enables exploring the conformational landscape of RNA with atomistic molecular dynamics and at the same time identifying RNA pockets in an efficient way with the aid of probes and enhanced-sampling techniques. In our benchmark composed of large, structured riboswitches as well as small, flexible viral RNAs, SHAMAN successfully identified all the experimentally resolved pockets and ranked them among the most favorite probe hotspots. Overall, SHAMAN sets a solid foundation for future drug design efforts targeting RNA with small molecules, effectively addressing the long-standing challenges in the field. <<<

Nanopore direct RNA sequencing (DRS) provides the direct access to native RNA strands with full-length information, shedding light on rich qualitative and quantitative properties of gene expression profiles. Here with NanoTrans, we present an integrated computational framework that comprehensively covers all major DRS-based application scopes, including isoform clustering and quantification, poly(A) tail length estimation, RNA modification profiling, and fusion gene detection. In addition to its merit in providing such a streamlined one-stop solution, NanoTrans also shines in its workflow-orientated modular design, batch processing capability, all-in-one tabular and graphic report output, as well as automatic installation and configuration supports. Finally, by applying NanoTrans to real DRS datasets of yeast, Arabidopsis, as well as human embryonic kidney and cancer cell lines, we further demonstrated its utility, effectiveness, and efficacy across a wide range of DRS-based application settings. <<<

DNA has been pursued as a compelling medium for digital data storage during the past decade. While large-scale data storage and random access have been achieved in artificial DNA, the synthesis cost keeps hindering DNA data storage from popularizing into daily life. In this study, we proposed a more efficient paradigm for digital data compressing to DNA, while excluding arbitrary sequence constraints. Both standalone neural networks and pre-trained language models were used to extract the intrinsic patterns of data, and generated probabilistic portrayal, which was then transformed into constraint-free nucleotide sequences with a hierarchical finite state machine. Utilizing these methods, a 12%-26% improvement of compression ratio was realized for various data, which directly translated to up to 26% reduction in DNA synthesis cost. Combined with the progress in DNA synthesis, our methods are expected to facilitate the realization of practical DNA data storage. <<<

AbstractPerceptual decisions should depend on sensory evidence. However, such decisions are also influenced by past choices and outcomes. These choice history biases may reflect advantageous strategies to exploit temporal regularities of natural environments. However, it is unclear whether and how observers can adapt their choice history biases to different temporal regularities, to exploit the multitude of temporal correlations that exist in nature. Here, we show that mice adapt their perceptual choice history biases to different temporal regularities. This adaptation is well captured by a normative reinforcement learning algorithm with multi-trial belief states, comprising both current trial sensory and previous trial memory states. We demonstrate that striatal dopamine tracks predictions of the model and behavior, pointing towards the involvement of dopamine in forming adaptive history biases. Our results reveal the adaptive nature of perceptual choice history biases, and shed light on their underlying computational principles and neural implementation. <<<

Zilong Zeng, Jiaying Zhang, Xinyuan Liang, Lianglong Sun, Yihe Zhang, Weiwei Men, Yanpei Wang, Rui Chen, Haibo Zhang, Shuping Tan, Jia-Hong Gao, Shaozheng Qin, Qiqi Tong, Hongjian He, Sha Tao, Qi Dong, Yong He, Tengda Zhao <<<

AbstractSusceptibility artifacts (SAs), which are inevitable for modern diffusion brain MR images with single-shot echo planar imaging (EPI) protocols in wide large-scale neuroimaging datasets, severely hamper the accurate detection of the human brain white matter structure. While several conventional and deep-learning based distortion correction methods have been proposed, the correction quality and model generality of these approaches are still limited. Here, we proposed the SACNet, a flexible SAs correction (SAC) framework for brain diffusion MR images of various phase-encoding EPI protocols based on an unsupervised learning-based registration convolutional neural network. This method could generate smooth diffeomorphic warps with optional neuroanatomy guidance to correct both geometric and intensity distortions of SAs. By employing near 2000 brain scans covering neonatal, child, adult and traveling participants, our SACNet consistently demonstrates state-of-the-art correction performance and effectively eliminates SAs-related multicenter effects compared with existing SAC methods. To facilitate the development of standard SAC tools for future neuroimaging studies, we also created easy-to-use command lines incorporating containerization techniques for quick user deployment. <<<

Joseph L. Watson , David Juergens , Nathaniel R. Bennett , Brian L. Trippe , Jason Yim , Helen E. Eisenach , Woody Ahern , Andrew J. Borst , Robert J. Ragotte , Lukas F. Milles , Basile I. M. Wicky , Nikita Hanikel , Samuel J. Pellock , Alexis Courbet , William Sheffler , Jue Wang , Preetham Venkatesh , Isaac Sappington , Susana Vázquez Torres , Anna Lauko , Valentin De Bortoli , Emile Mathieu , Regina Barzilay , Tommi S. Jaakkola , Frank DiMaio , Minkyung Baek , David Baker <<<

AbstractThere has been considerable recent progress in designing new proteins using deep learning methods1–9. Despite this progress, a general deep learning framework for protein design that enables solution of a wide range of design challenges, includingde novobinder design and design of higher order symmetric architectures, has yet to be described. Diffusion models10,11have had considerable success in image and language generative modeling but limited success when applied to protein modeling, likely due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding, and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold Diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of new designs. In a manner analogous to networks which produce images from user-specified inputs, RFdiffusionenables the design of diverse, complex, functional proteins from simple molecular specifications. <<<

AbstractFor both humans and machines, the essence of learning is to pinpoint which components in its information processing pipeline are responsible for an error in its output — a challenge that is known ascredit assignment. How the brain solves credit assignment is a key question in neuroscience, and also of significant importance for artificial intelligence. It has long been assumed that credit assignment is best solved by backpropagation, which is also the foundation of modern machine learning. However, it has been questioned whether it is possible for the brain to implement backpropagation and learning in the brain may actually be more efficient and effective than backpropagation. Here, we set out a fundamentally different principle on credit assignment, calledprospective configuration. In prospective configuration, the network first infers the pattern of neural activity that should result from learning, and then the synaptic weights are modified to consolidate the change in neural activity. We demonstrate that this distinct mechanism, in contrast to backpropagation, (1) underlies learning in a well-established family of models of cortical circuits, (2) enables learning that is more efficient and effective in many contexts faced by biological organisms, and (3) reproduces surprising patterns of neural activity and behaviour observed in diverse human and animal learning experiments. Our findings establish a new foundation for learning beyond backpropagation, for both understanding biological learning and building artificial intelligence. <<<

AbstractNo sooner is an experience over than its neural memory representation begins to be strengthened and transformed through the process of memory replay. Using fMRI, we examined how memory strength manipulated through repetition during encoding modulates post-encoding replay in humans. Results revealed that repetition did not increase replay frequency in the hippocampus. However, replay in cortical regions and hippocampal-cortical coordinated replay were significantly enhanced for repeated events, suggesting that repetition accelerates the consolidation process. Interestingly, we found that replay frequency in both hippocampus and cortex modulated behavioral success on an immediate associative recognition test for the weakly encoded information, indicating a significant role for post-encoding replay in rescuing once-presented events. Together, our findings highlight the relationships of replay to stabilizing weak memories and accelerating cortical consolidation for strong memories. <<<

AbstractRecent human electrophysiology work has uncovered the presence of high frequency oscillatory events, termed ripples, during awake behavior. This prior work focuses on ripples in the medial temporal lobe (MTL) during memory retrieval. Few studies, however, investigate ripples during item encoding. Many studies have found neural activity during encoding that predicts later recall, termed subsequent memory effects (SMEs), but it is unclear if ripples during encoding also predict subsequent recall. Detecting ripples in 124 neurosurgical participants performing an episodic memory task, we find insignificant ripple SMEs in any MTL region, even as these regions exhibit robust high frequency activity (HFA) SMEs. Instead, hippocampal ripples increase during encoding of items leading to recall of temporally or semantically associated items, a phenomenon known as clustering. This subsequent clustering effect (SCE) arises specifically when hippocampal ripples occur during both encoding and retrieval, suggesting that ripples mediate the encoding and future reinstatement of episodic memories. <<<

Andreas Schuh , Antonios Makropoulos , Emma C. Robinson , Lucilio Cordero-Grande , Emer Hughes , Jana Hutter , Anthony N. Price , Maria Murgasova , Rui Pedro A. G. Teixeira , Nora Tusor , Johannes K. Steinweg , Suresh Victor , Mary A. Rutherford , Joseph V. Hajnal , A. David Edwards , Daniel Rueckert <<<

AbstractPremature birth increases the risk of developing neurocognitive and neurobe-havioural disorders. The mechanisms of altered brain development causing these disorders are yet unknown. Studying the morphology and function of the brain during maturation provides us not only with a better understanding of normal development, but may help us to identify causes of abnormal development and their consequences. A particular difficulty is to distinguish abnormal patterns of neurodevelopment from normal variation. The Developing Human Connectome Project (dHCP) seeks to create a detailed four-dimensional (4D) connectome of early life. This connectome may provide insights into normal as well as abnormal patterns of brain development. As part of this project, more than a thousand healthy fetal and neonatal brains will be scanned in vivo. This requires computational methods which scale well to larger data sets. We propose a novel groupwise method for the construction of a spatio-temporal model of mean morphology from cross-sectional brain scans at different gestational ages. This model scales linearly with the number of images and thus improves upon methods used to build existing public neonatal atlases, which derive correspondence between all pairs of images. By jointly estimating mean shape and longitudinal change, the atlas created with our method overcomes temporal inconsistencies, which are encountered when mean shape and intensity images are constructed separately for each time point. Using this approach, we have constructed a spatio-temporal atlas from 275 healthy neonates between 35 and 44 weeks post-menstrual age (PMA). The resulting atlas qualitatively preserves cortical details significantly better than publicly available atlases. This is moreover confirmed by a number of quantitative measures of the quality of the spatial normalisation and sharpness of the resulting template brain images. <<<

AbstractIn this paper, we present a deep learning method, DDMReg, for accurate registration between diffusion MRI (dMRI) datasets. In dMRI registration, the goal is to spatially align brain anatomical structures while ensuring that local fiber orientations remain consistent with the underlying white matter fiber tract anatomy. DDMReg is a novel method that uses joint whole-brain and tract-specific information for dMRI registration. Based on the successful VoxelMorph framework for image registration, we propose a novel registration architecture that leverages not only whole brain information but also tract-specific fiber orientation information. DDMReg is an unsupervised method for deformable registration between pairs of dMRI datasets: it does not require nonlinearly pre-registered training data or the corresponding deformation fields as ground truth. We perform comparisons with four state-of-the-art registration methods on multiple independently acquired datasets from different populations (including teenagers, young and elderly adults) and different imaging protocols and scanners. We evaluate the registration performance by assessing the ability to align anatomically corresponding brain structures and ensure fiber spatial agreement between different subjects after registration. Experimental results show that DDMReg obtains significantly improved registration performance compared to the state-of-the-art methods. Importantly, we demonstrate successful generalization of DDMReg to dMRI data from different populations with varying ages and acquired using different acquisition protocols and different scanners. <<<

Motivation: Gene overlap occurs when two or more genes are encoded by the same nucleotides. This phenomenon is found in all taxonomic domains, but is particularly common in viruses, where it may provide a mechanism to increase the information content of compact genomes. The presence of overlapping reading frames (OvRFs) can skew estimates of selection based on the rates of non-synonymous and synonymous substitutions, since a substitution that is synonymous in one reading frame may be non-synonymous in another, and vice versa. Results: To understand the impact of OvRFs on molecular evolution, we implemented a versatile simulation model of nucleotide sequence evolution along a phylogeny with an arbitrary distribution of reading frames. We use a custom data structure to track the substitution rates at every nucleotide site, which is determined by the stationary nucleotide frequencies, transition bias, and the distribution of selection biases (dN/dS) in the respective reading frames. Availability and implementation: Our simulation model is implemented in the Python scripting language. All source code is released under the GNU General Public License (GPL) version 3, and is available at https://github.com/PoonLab/HexSE. <<<

Graham Scott Erwin , Gamze Gursoy , Rashid Al-Abri , Ashwini Suriyaprakash , Egor Dolzhenko , Kevin Zhu , Christian R Hoerner , Shannon M White , Lucia Ramirez , Ananya Vadlakonda , Alekhya Vadlakonda , Konor von Kraut , Julia Park , Charlotte M Brannon , Daniel A Sumano , Raushun A Kirtikar , Alicia A Erwin , Thomas J Metzner , Ryan K. C. Yuen , Alice C Fan , John T Leppert , Michael A Eberle , Mark Gerstein , Michael P Snyder <<<

Expansion of a single repetitive DNA sequence, termed a tandem repeat (TR), is known to cause more than 50 diseases. However, repeat expansions are often not explored beyond neurological and neurodegenerative disorders. In some cancers, mutations accumulate in short tracts of TRs (STRs), a phenomenon termed microsatellite instability (MSI); however larger repeat expansions have not been systematically analyzed in cancer. Here, we identified TR expansions in 2,622 cancer genomes, spanning 29 cancer types. In 7 cancer types, we found 160 recurrent repeat expansions (rREs); most of these (155/160) were subtype specific. We found that rREs were non-uniformly distributed in the genome with an enrichment near candidate cis-regulatory elements, suggesting a role in gene regulation. One rRE located near a regulatory element in the first intron of UGT2B7 was detected in 34% of renal cell carcinoma samples and was validated by long-read DNA sequencing. Moreover, targeting cells harboring this rRE with a rationally designed, sequence-specific DNA binder led to a dose-dependent decrease in cell proliferation. Overall, our results demonstrate that rREs are an important but unexplored source of genetic variation in human cancers, and we provide a comprehensive catalog for further study. <<<

Carolina Villarroya-Beltri , Ana Osorio , Raúl Torres-Ruiz , David Gómez-Sánchez , Marianna Trakala , Agustin Sánchez-Belmonte , Fátima Mercadillo , Borja Pitarch , Almudena Hernández-Núñez , Antonio Gómez-Caturla , Daniel Rueda , José Perea , Sandra Rodríguez-Perales , Marcos Malumbres , Miguel Urioste <<<

Aneuploidy is a frequent feature of human tumors. Germline mutations leading to aneuploidy are very rare in humans, and their tumor-promoting properties are mostly unknown at the molecular level. We report here novel germline biallelic mutations in MAD1L1, the gene encoding the Spindle Assembly Checkpoint (SAC) protein MAD1, in a 36-year-old female with a dozen of neoplasias, including five malignant tumors. Functional studies in peripheral blood cells demonstrated lack of full-length protein and deficient SAC response, resulting in ∼30-40% of aneuploid cells as detected by cytogenetic and single-cell (sc) DNA analysis. scRNA-seq analysis of patient blood cells identified mitochondrial stress accompanied by systemic inflammation with enhanced interferon and NFkB signaling. The inference of chromosomal aberrations from scRNA-seq analysis detected inflammatory signals both in aneuploid and euploid cells, suggesting a non-cell autonomous response to aneuploidy. In addition to random aneuploidies, MAD1L1 mutations resulted in specific clonal expansions of γδ T-cells with chromosome 18 gains and enhanced cytotoxic profile, as well as intermediate B-cells with chromosome 12 gains and transcriptomic signatures characteristic of chronic lymphocytic leukemia cells. These data point to MAD1L1 mutations as the cause of a new aneuploidy syndrome with systemic inflammation and unprecedented tumor susceptibility. <<<