bioRxiv : the preprint server for biology最新文献

Neural activity in the dentate gyrus (DG) is required for the detection and discrimination of novelty, context and patterns, amongst other cognitive processes. Prior work has demonstrated that there are differences in the activation of granule neurons in the supra and infrapyramidal blades of the DG during a range of hippocampal dependent tasks. Here we used an automated touch screen pattern separation task combined to temporally controlled tagging of active neurons to determine how performance in a cognitively demanding task affected patterns of neural activity in the DG. We found an increase in the blade-biased activity of suprapyramidal mature granule cells (mGCs) during the performance of a high cognitive demand segment of the task, with a further characteristic distribution of active neurons along the apex to blade, and hilar to molecular layer axes. Chemogenetic inhibition of adult-born granule cells (abDGCs) beyond a critical window of their maturation significantly impaired performance of mice during high-demand conditions but not when cognitive demand was low. abDGC inhibition also elevated the total activity of mGCs and disturbed the patterned distribution of active mGCs even in mice that eventually succeeded in the task. Conversely chemogenetic inhibition of mGCs reduced success in the high cognitive demand portion of this task and decreased the global number of active GCs without affecting the patterned distribution of active cells. These findings demonstrate how a high cognitive demand pattern separation task preferentially activates mGCs in subregions of the DG and are consistent with a modulatory role for abDGCs on the dentate circuit which in part governs the spatially organized patterns of activity of mGCs.

Appetitive and aversive stimuli evoke approach and avoidance behaviors essential for survival and well-being. While affective processing has been extensively examined in terms of arousal and valence, the extent to which value processing is independent from arousal and valence processing in naturalistic contexts remains unclear. We addressed this gap using a naturalistic approach-avoidance task. Ninety-one human participants underwent functional MRI scanning while engaging in approach-avoidance tasks involving two levels of threat (mild or aversive electrical stimulation) and reward (monetary gains). We estimated effect sizes (Cohen's D) across subjects for increasing levels of threat, reward and arousal; for valence (negative vs positive); and for valence-arousal interactions. Effect sizes for threat and reward were strongly positively correlated across brain voxels (r = 0.82), suggesting a strong influence of a shared factor. Spatial independent component analysis decomposed these effect sizes into two independent latent factors, one that represented arousal processing and another that exhibited characteristics of value processing. Importantly, we predicted that valence-arousal interaction effects would increase with latent value effects across voxels, since both valence and arousal contribute to our overall valuation process. We indeed found this to be true. Furthermore, sizable latent value effects were observed in dorsolateral prefrontal cortex, fusiform gyrus and middle temporal gyrus, areas also involved in attention and executive control. Thus, our findings revealed a value system in the human brain that could operate independently of arousal and valence systems during naturalistic approach-avoidance behaviors, providing new insights into the neural mechanisms of affective processing.

Animals with small nervous systems have a limited number of sensory neurons that must encode information from a changing environment. This problem is particularly exacerbated in nematodes that populate a wide variety of distinct ecological niches but only have a few sensory neurons available to encode multiple modalities. How does sensory diversity prevail within this constraint in neuron number? To identify the genetic basis for patterning different nervous systems, we demonstrate that sensory neurons in Pristionchus pacificus respond to various salt sensory cues in a manner that is partially distinct from that of the distantly related nematode Caenorhabditis elegans. By visualizing neuronal activity patterns, we show that contrary to previous expectations based on its genome sequence, the salt responses of P. pacificus are encoded in a left/right asymmetric manner in the bilateral ASE neuron pair. Our study illustrates patterns of evolutionary stability and change in the gustatory system of nematodes.

We address the problem of predicting high detail RNA structure geometry from the information available in low resolution experimental maps of electron density. Here low resolution refers to ≥2.5Å where the location of the phosphate groups and the glyocosidic bonds can be determined from electron density but all other backbone atom positions cannot. In contrast, high resolution determines all backbone atomic positions. To this end, we firstly create a gold standard data base for four groups of manually corrected suites, each reflecting one out of four sugar pucker-pair configurations. Secondly we develop and employ a modified version of the previously devised algorithm MINT-AGE to learn clusters that are in high correspondence with gold standard's conformational classes based on 3D RNA structure. Since some of the manually corrected classes are of very small size, the modified version of MINT-AGE is able to also identify very small clusters. Thirdly, the new algorithm RNAprecis assigns low resolution structures to newly designed 3D shape coordinates. Our improvements include: (i) learned classes augmented to cover also very low sample sizes and (ii) regularizing a key distance by introducing an adaptive Mahalanobis distance. On a test data containing many clashing and suites modeled as conformational outliers, RNA precis shows good results suggesting that our learning method generalizes well. In particular, our modified MINT-AGE clustering can be finer than the existing curated gold standard suite conformers. For example, the 0a conformer has been separated into two clusters seen in different structural contexts. Such new distinctions can have implications for biochemical interpretation of RNA structure.

Machine learning holds immense promise in biology, particularly for the challenging task of identifying causal variants for Mendelian and complex traits. Two primary approaches have emerged for this task: supervised sequence-to-function models trained on functional genomics experimental data and self-supervised DNA language models that learn evolutionary constraints on sequences. However, the field currently lacks consistently curated datasets with accurate labels, especially for non-coding variants, that are necessary to comprehensively benchmark these models and advance the field. In this work, we present TraitGym, a curated dataset of regulatory genetic variants that are either known to be causal or are strong candidates across 113 Mendelian and 83 complex traits, along with carefully constructed control variants. We frame the causal variant prediction task as a binary classification problem and benchmark various models, including functional-genomics-supervised models, self-supervised models, models that combine machine learning predictions with curated annotation features, and ensembles of these. Our results provide insights into the capabilities and limitations of different approaches for predicting the functional consequences of non-coding genetic variants. We find that alignment-based models CADD and GPN-MSA compare favorably for Mendelian traits and complex disease traits, while functional-genomics-supervised models Enformer and Borzoi perform better for complex non-disease traits. Evo2 shows substantial performance gains with scale, but still lags somewhat behind alignment-based models, struggling particularly with enhancer variants. The benchmark, including a Google Colab notebook to evaluate a model in a few minutes, is available at https://huggingface.co/datasets/songlab/TraitGym.

The price, quality and throughout of DNA sequencing continue to improve. Algorithmic innovations have allowed inference of a growing range of features from DNA sequencing data, quantifying nuclear, mitochondrial and evolutionary aspects of both germline and somatic genomes. To automate analyses of the full range of genomic characteristics, we created an extensible Nextflow meta-pipeline called metapipeline-DNA. Metapipeline-DNA analyzes targeted and whole-genome sequencing data from raw reads through pre-processing, feature detection by multiple algorithms, quality-control and data-visualization. Each step can be run independently and is supported robust software engineering including automated failure-recovery, robust testing and consistent verifications of inputs, outputs and parameters. Metapipeline-DNA is cloud-compatible and highly configurable, with options to subset and optimize each analysis. Metapipeline-DNA facilitates high-scale, comprehensive analysis of DNA sequencing data.

Hippocampal sharp-wave ripples (SPW-Rs) are high-frequency oscillations critical for memory consolidation in mammals. Despite extensive characterization in rodents, their application as biomarkers to track and treat memory dysfunction in humans is limited by coarse spatial sampling, interference from interictal epileptiform discharges (IEDs), and lack of consensus on human SPW-R localization and morphology. We demonstrate that mouse and human hippocampal ripples share spatial, spectral and temporal features, which are clearly distinct from IEDs. In 1024-channel hippocampal recordings from APP/PS1 mice, SPW-Rs were distinguishable from IEDs by their narrow localization to the CA1 pyramidal layer, narrowband frequency peaks, and multiple ripple cycles on the unfiltered local field potential. In epilepsy patients, ripples showed similar narrowband frequency peaks and visible ripple cycles in CA1 and the subiculum but were absent in the dentate gyrus. Conversely, IEDs showed a broad spatial extent and wide-band frequency power. We introduce a semi-automated, human ripple detection toolbox ("ripmap") selecting optimal detection channels and separating event waveforms by low-dimensional embedding. Our approach improves ripple detection accuracy, providing a firm foundation for future human memory research.

Dedifferentiation programs are commonly enacted during breast cancer progression to enhance tumor cell fitness. Increased cellular plasticity within the neoplastic compartment of tumors correlates with disease aggressiveness, often culminating in greater resistance to cytotoxic therapies or augmented metastatic potential. Here we report that subpopulations of dedifferentiated neoplastic breast epithelial cells express canonical leukocyte cell surface receptor proteins and have thus named this cellular program "immune mimicry." We document neoplastic cells engaging in immune mimicry within public human breast tumor single-cell RNA-seq datasets, histopathological breast tumor specimens, breast cancer cell lines, as well as in murine transgenic and cell line-derived mammary cancer models. Immune-mimicked neoplastic cells harbor hallmarks of dedifferentiation and are enriched in treatment-resistant and high-grade breast tumors. We corroborated these observations in aggressive breast cancer cell lines where anti-proliferative cytotoxic chemotherapies drove epithelial cells toward immune mimicry. Moreover, in subsequent proof-of-concept studies, we demonstrate that expression of the CD69 leukocyte activation protein by neoplastic cells confers a proliferative advantage that facilitates early tumor growth and therefore conclude that neoplastic breast epithelial cells upregulating leukocyte surface receptors potentiate malignancy. Moving forward, neoplastic immune mimicry should be evaluated for prognostic utility in breast cancer to determine stratification potential for patients with increased risks of tumor recurrence, metastasis, and therapeutic resistance.

Statement of significance: Neoplastic breast epithelial cells express surface receptors canonically attributed to leukocytes and are associated with therapy resistance and aggressive tumor behavior.

Of the 1.5 million emergency room visits each year in the United States due to flexor tendon injuries in the hand, over 30-40% result in peritendinous adhesions which can limit range of motion (ROM) and severely impact an individual's quality of life. Adhesions are fibrous scar-like tissues which can form between adjacent tissues in the body in response to injury, inflammation, or during normal healing following surgery. Currently, there is no widespread solution for adhesion prevention in the delicate space of the digit while allowing a patient full ROM quickly after surgery. There is a clear clinical need for a material capable of limiting adhesion formation which is simple to apply, does not impair healing, remains at the application site during motion and initial inflammation (days - weeks), and leaves tendon glide unencumbered. In this work, we developed dynamically crosslinked, bioresorbable supramolecular hydrogels as easy-to-apply lubricious barriers to prevent the formation of peritendinous adhesions. These hydrogels exhibit excellent long-term stability, injectability, and thermally stable viscoelastic properties that allow for simple storage and facile application. We evaluated interactions at the interface of the hydrogels and relevant tissues, including human tendon and skin, in shear and extensional stress modes and demonstrated a unique mechanism of adhesion prevention based on maintenance of a lubricious hydrogel barrier between tissues. Ex vivo studies show that the hydrogels did not impair the gliding behavior nor mechanical properties of tendons when applied in cadaveric human hands following clinically relevant flexor tendon repair. We further applied these hydrogels in a preclinical rat Achilles tendon injury model and observed prolonged local retention at the repair site as well as improved recovery of key functional metrics, including ROM and maximal dorsiflexion. Further, these hydrogels were safe and did not impair tendon strength nor healing compared to the current standard of care. These dynamic, biocompatible hydrogels present a novel solution to the significant problem of peritendinous adhesions with clear translational potential.