bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mice require proprioception to establish long-term visuospatial memory
Authors: Rutler Olivia , Persaud Serena , Kosmidis Stylianos , Park Jung M. , Harano Nina , Bruno Randy M. , Goldberg Michael E. 
Published: October 2023

AbstractBecause the retina moves constantly, the retinotopic representation of the visual world is spatially inaccurate and the brain must transform this spatially inaccurate retinal signal to a spatially accurate signal usable for perception and action. One of the salient discoveries of modern neuroscience is the role of the hippocampus in establishing gaze-independent, long-term visuospatial memories. The rat hippocampus has neurons which report the animal’s position in space regardless of its angle of gaze. Rats with hippocampal lesions are unable to find the location of an escape platform hidden in a pool of opaque fluid, the Morris Water Maze (MWM) based on the visual aspects of their surrounding environment. Here we show that the representation of proprioception in the dysgranular zone of primary somatosensory cortex is equivalently necessary for mice to learn the location of the hidden platform, presumably because without it they cannot create a long-term gaze-independent visuospatial representation of their environment from the retinal signal. They have no trouble finding the platform when it is marked by a flag, and they have no motor or vestibular deficits.
Single-cell chromatin state transitions during epigenetic memory formation
Authors: Fujimori Taihei , Rios-Martinez Carolina , Thurm Abby R. , Hinks Michaela M. , Doughty Benjamin R. , Sinha Joydeb , Le Derek , Hafner Antonina , Greenleaf William J. , Boettiger Alistair N. , Bintu Lacramioara 
Published: October 2023

AbstractRepressive chromatin modifications are thought to compact chromatin to silence transcription. However, it is unclear how chromatin structure changes during silencing and epigenetic memory formation. We measured gene expression and chromatin structure in single cells after recruitment and release of repressors at a reporter gene. Chromatin structure is heterogeneous, with open and compact conformations present in both active and silent states. Recruitment of repressors associated with epigenetic memory produces chromatin compaction across 10-20 kilobases, while reversible silencing does not cause compaction at this scale. Chromatin compaction is inherited, but changes molecularly over time from histone methylation (H3K9me3) to DNA methylation. The level of compaction at the end of silencing quantitatively predicts epigenetic memory weeks later. Similarly, chromatin compaction at the Nanog locus predicts the degree of stem-cell fate commitment. These findings suggest that the chromatin state across tens of kilobases, beyond the gene itself, is important for epigenetic memory formation.
Benchmark dataset for training machine learning models to predict the pathway involvement of metabolites
Authors: Huckvale Erik D. , Powell Christian D. , Jin Huan , Moseley Hunter N.B. 
Published: October 2023

AbstractMetabolic pathways are a human-defined grouping of life sustaining biochemical reac-tions, metabolites being both the reactants and products of these reactions. But many public datasets include identified metabolites whose pathway involvement is unknown, hindering metabolic inter-pretation. To address these shortcomings, various machine learning models, including those trained on data from the Kyoto Encyclopedia of Genes and Genomes (KEGG), have been developed to pre-dict the pathway involvement of metabolites based on their chemical descriptions; however, these prior models are based on old metabolite KEGG-based datasets, including one benchmark dataset that is invalid due to the presence of over 1500 duplicate entries. Therefore, we have developed a new benchmark dataset derived from the KEGG following optimal standards of scientific compu-tational reproducibility and including all source code needed to update the benchmark dataset as KEGG changes. We have used this new benchmark dataset with our atom coloring methodology to develop and compare the performance of Random Forest, XGBoost, and multilayer perceptron with autoencoder models generated from our new benchmark dataset. Best overall weighted average performance across 1000 unique folds was an F1-score of 0.8180 and Matthews correlation coeffi-cient of 0.7933, which was provided by XGBoost binary classification models for 11 KEGG-defined pathway categories.
Type I IFN signaling in the absence of IRGM1 promotesM. tuberculosisreplication in immune cells by suppressing T cell responses
Authors: Naik Sumanta K. , McNehlan Michael E. , Mreyoud Yassin , Kinsella Rachel L. , Smirnov Asya , Chowdhury Chanchal Sur , McKee Samuel R. , Dubey Neha , Woodson Reilly , Kreamalmeyer Darren , Stallings Christina L. 
Published: October 2023

ABSTRACTPolymorphisms in theIRGMgene are associated with susceptibility to tuberculosis in humans. A murine ortholog ofIrgm,Irgm1, is also essential for controllingMycobacterium tuberculosis(Mtb) infection in mice. Multiple processes have been associated with IRGM1 activity that could impact the host response to Mtb infection, including roles in autophagy-mediated pathogen clearance and expansion of activated T cells. However, what IRGM1-mediated pathway is necessary to control Mtb infectionin vivoand the mechanistic basis for this control remains unknown. We dissected the contribution of IRGM1 to immune control of Mtb pathogenesisin vivoand found thatIrgm1deletion leads to higher levels of IRGM3-dependent type I interferon signaling. The increased type I interferon signaling precludes T cell expansion during Mtb infection. The absence of Mtb-specific T cell expansion inIrgm1-/-mice results in uncontrolled Mtb infection in neutrophils and alveolar macrophages, which directly contributes to susceptibility to infection. Together, our studies reveal that IRGM1 is required to promote T cell-mediated control of Mtb infection in neutrophils, which is essential for the survival of Mtb-infected mice. These studies also uncover new ways type I interferon signaling can impact TH1 immune responses.
Consequences of directly- and indirectly-experienced heat stress in a mutagenic environment
Authors: Feugere Lauric , De Freitas Claudio Silva , Bates Adam , Storey Kenneth B. , Beltran-Alvarez Pedro , Valero Katharina C. Wollenberg 
Published: October 2023

AbstractClimate change increases the frequency and duration of heat events. Negative effects of heat stress may be exacerbated through the action of social metabolites between aquatic animals. Whilst early life stages are vulnerable to stress-induced damage, they deploy cellular mechanisms to protect cells against mutagens such as ultraviolet rays (UV). Little is known about the fate of fish embryos which have experienced heat stress in a mutagenic environment. The present study exposed zebrafish embryos to one of three stress history treatments consisting of direct heat stress (TS+UV), the social context of heat stress via social metabolites (SM+UV), and their combination in TS+SM+UV before a UVB/UVA damage/repair assay. We measured phenotypic and transcriptomic responses to these treatments, and estimated mutational damage through DNA mutation frequencies and RNA integrity values. Compared to UV-treated controls (C+UV), the social context of heat stress history preceding the UV assay altered keratin and cell structuring-related pathways, associated with longer embryos with over-developed pericardia displaying behavioural hypoactivity. Relative to C+UV, direct heat stress history preceding UV exposure had a hormetic effect by stimulating the cellular stress response and facilitating DNA repair, which rescued embryos from subsequent UV damage and improved their apparent fitness. However, heat stress combined with social metabolites overwhelmed embryos in the UV assay, which annihilated the hormetic effect, introduced mutations, and lowered their apparent fitness. Whilst generated in the laboratory, these findings provide an important baseline for understanding the consequences of heat stress history in natural environments, where heat stress occurs within a social context.HighlightsHeat stress had a hormetic effect against UV damage, by stimulating the heat shock response, antioxidants, and DNA repair.The heat hormetic effect protected and/or rescued embryos from UV damage by reducing single nucleotide variants observed in RNA, lowering malformations, and accelerating development.Heat-stressed embryos released social metabolites that initiated keratin, immune, and cellular structuring responses in receivers, in turn increasing body sizes but without reducing UV-induced malformations.Heat combined with social metabolites overwhelmed embryos in response to UV, reducing fitness-relevant performance.Heat stress during early embryogenesis led to differential fitness-relevant outcomes showing a nonlinear relationship with stress intensity.Summary statementSublethal heat stress protects zebrafish embryos in a mutagenic environment, but this protective effect is lost when zebrafish embryos additionally stress each other via chemical cues.Graphical abstract
Integrating Genomics, Collections, and Community Science to Reveal Speciation in a Variable Monitor Lizard (Varanus tristis)
Authors: Pavón-Vázquez Carlos J. , Fitch Alison J. , Doughty Paul , Donnellan Stephen C. , Keogh J. Scott 
Published: October 2023

Abstract—The accurate characterization of species diversity is a vital prerequisite for ecological and evolutionary research, as well as conservation. Thus, it is necessary to generate robust hypotheses of species limits based on the inference of evolutionary processes. Integrative species delimitation, the inference of species limits based on multiple sources of evidence, can provide unique insight into species diversity and the processes behind it. However, the application of integrative approaches in non-model organisms is often limited by the amount of data that is available. Here, we show how data relevant for species delimitation can be bolstered by incorporating information from tissue collections, museum specimens, and observations made by the wider community. We show how to integrate these data under a hypothesis-driven, integrative framework by identifying the processes generating genetic and phenotypic variation inVaranus tristis, a widespread and variable complex of Australian monitor lizards. Using genomic, morphometric (linear and geometric), coloration, spatial, and environmental data we show that disparity in this complex is inconsistent with intraspecific variation and instead suggests that speciation has occurred. Based on our results, we identify the environmental factors that may have been responsible for the geographic sorting of variation. Our workflow provides a guideline for the integrative analysis of several types of data to identify the occurrence and causes of speciation. Furthermore, our study highlights how community science and machine learning—two tools used here—can be used to accelerate taxonomic research.
CellPLM: Pre-training of Cell Language Model Beyond Single Cells
Authors: Wen Hongzhi , Tang Wenzhuo , Dai Xinnan , Ding Jiayuan , Jin Wei , Xie Yuying , Tang Jiliang 
Published: October 2023

AbstractThe current state-of-the-art single-cell pre-trained models are greatly inspired by the success of large language models. They trained transformers by treating genes as tokens and cells as sentences. However, three fundamental differences between single-cell data and natural language data are overlooked: (1) scRNA-seq data are presented as bag-of-genes instead of sequences of RNAs; (2) Cell-cell relations are more intricate and important than inter-sentence relations; and (3) The quantity of single-cell data is considerably inferior to text data, and they are very noisy. In light of these characteristics, we propose a new pre-trained modelCellPLM, which takes cells as tokens and tissues as sentences. In addition, we leverage spatially-resolved transcriptomic data in pre-training to facilitate learning cell-cell relationships and introduce a Gaussian mixture prior distribution as an additional inductive bias to overcome data limitation.CellPLMis the first single-cell pre-trained transformer that encodes cell-cell relations and it consistently outperforms existing pre-trained and non-pre-trained models in diverse downstream tasks, with 100x times higher inference speed compared to existing pre-trained models.
Orchards and paddy differentially impact rock outcrop amphibians: Insights from community- and species-level responses
Authors: Jithin Vijayan , Rane Manali , Watve Aparna , Naniwadekar Rohit 
Published: October 2023

ABSTRACTWith agricultural demands increasing globally, determining the nature of impacts of different forms of agriculture on biodiversity, especially for threatened vertebrates and habitats, is critical to inform land management. We determined the impacts of converting rock outcrops (a habitat more threatened than rainforests) to orchards and paddy on anurans in the Western Ghats biodiversity hotspot. We sampled 50 belt transects four times across four sites during the rainy season and recorded information on amphibians and their microhabitats. We determined community-level responses using Hill numbers, beta-diversity measures, and non-metric multidimensional scaling, and species-level responses using joint species distribution modelling. Converting rock outcrops to paddy and orchards significantly altered microhabitat availability. Conversion to paddy mostly had community-level impacts, i.e., lowered species richness and more nested communities, whereas conversion to orchards mostly had species-level impacts, i.e., lowered species occurrence, highlighting the differential impacts of different forms of agriculture on amphibians and the need to determine impacts of land-use change on communities and species concurrently. We show that large rock pools are critical microhabitats for anurans as they serve as a refuge and protect anurans from desiccation during dry spells, which may be prolonged by climate change. Since rock outcrop habitats in low elevations are rapidly being converted to orchards, efforts are needed to conserve them in partnership with local communities, the custodians of these habitats. Our findings demonstrate that different forms of agriculture can have divergent impacts on biodiversity, and determining their impacts may require assessments at multiple scales, from species to communities.
Estimating protein-ligand interactions with geometric deep learning and mixture density models
Authors: Kalakoti Yogesh , Gawande Swaraj , Sundar Durai 
Published: October 2023

AbstractUnderstanding the interactions between a ligand and its molecular target is crucial in guiding the optimization of molecules for anyin-silicodrug-design workflow. Multiple experimental and computational methods have been developed to better understand these intermolecular interactions. With the availability of a large number of structural datasets, there is a need for developing statistical frameworks that improve upon existing physics-based solutions. Here, we report a method based on geometric deep learning that is capable of predicting the binding conformations of ligands to protein targets. A technique to generate graphical representations of protein was developed to exploit the topological and electrostatic properties of the binding region. The developed framework, based on graph neural networks, learns a statistical potential based on the distance likelihood, which is tailor-made for each ligand–target pair. This potential can be coupled with global optimization algorithms such as differential evolution to reproduce the experimental binding conformations of ligands. We show that the potential based on distance likelihood, described here, performs similarly or better than well-established scoring functions for docking and screening tasks. Overall, this method represents an example of how artificial intelligence can be used to improve structure-based drug design.Significance statementCurrent machine learning-based solutions to model protein-ligand interactions lack the level of interpretability that physics-based methods usually provide. Here, a workflow to embed protein binding surfaces as graphs was developed to serve as a viable data structure to be processed by geometric deep learning. The developed architecture based on mixture density models was employed to accurately estimate the position and conformation of the small molecule within the binding region. The likelihood-based scoring function was compared against existing physics-based alternatives, and significant performance improvements in terms of docking power, screening power and reverse screening power were observed. Taken together, the developed framework provides a platform for utilising geometric deep-learning models for interpretable prediction of protein-ligand interactions at a residue level.
The Role of Ryanodine Receptor 2 in Drug-Associated Learning
Authors: Barber Kara R. , Vizcarra Velia S. , Zilch Ashlyn , Majuta Lisa , Diezel Cody C. , Culver Oliver P. , Hughes Brandon W. , Taniguchi Makoto , Streicher John M. , Vanderah Todd W. , Riegel Arthur C. 
Published: October 2023

AbstractType-2 ryanodine receptor (RyR2) ion channels facilitate the release of Ca2+from stores and serve an important function in neuroplasticity. The role for RyR2 in hippocampal-dependent learning and memory is well established and chronic hyperphosphorylation of RyR2 (RyR2P) is associated with pathological calcium leakage and cognitive disorders, including Alzheimer’s disease. By comparison, little is known about the role of RyR2 in the ventral medial prefrontal cortex (vmPFC) circuitry important for working memory, decision making, and reward seeking. Here, we evaluated the basal expression and localization of RyR2 and RyR2P in the vmPFC. Next, we employed an operant model of sucrose, cocaine, or morphine self-administration (SA) followed by a (reward-free) recall test, to reengage vmPFC neurons and reactivate reward-seeking and re-evaluated the expression and localization of RyR2 and RyR2P in vmPFC. Under basal conditions, RyR2 was expressed in pyramidal cells but not regularly detected in PV/SST interneurons. On the contrary, RyR2P was rarely observed in PFC somata and was restricted to a different subcompartment of the same neuron - the apical dendrites of layer-5 pyramidal cells. Chronic SA of drug (cocaine or morphine) and nondrug (sucrose) rewards produced comparable increases in RyR2 protein expression. However, recalling either drug reward impaired the usual localization of RyR2P in dendrites and markedly increased its expression in somata immunoreactive for Fos, a marker of highly activated neurons. These effects could not be explained by chronic stress or drug withdrawal and instead appeared to require a recall experience associated with prior drug SA. In addition to showing the differential distribution of RyR2/RyR2P and affirming the general role of vmPFC in reward learning, this study provides information on the propensity of addictive drugs to redistribute RyR2P ion channels in a neuronal population engaged in drug-seeking. Hence, focusing on the early impact of addictive drugs on RyR2 function may serve as a promising approach to finding a treatment for substance use disorders.
Interpreting Molecular Dynamics Forces as Deep Learning Gradients Improves Quality Of Predicted Protein Structures
Authors: King Jonathan Edward , Koes David Ryan 
Published: October 2023

ABSTRACTProtein structure predictions from deep learning models like AlphaFold2, despite their remarkable accuracy, are likely insufficient for direct use in downstream tasks like molecular docking. The functionality of such models could be improved with a combination of increased accuracy and physical intuition. We propose a new method to train deep learning protein structure prediction models using molecular dynamics force fields to work toward these goals. Our custom PyTorch loss function, OpenMM-Loss, represents the potential energy of a predicted structure. OpenMM-Loss can be applied to any all-atom representation of protein structure capable of mapping into our software package, SidechainNet. We demonstrate our method’s efficacy by finetuning OpenFold. We show that subsequently predicted protein structures, both before and after a relaxation procedure, exhibit comparable accuracy while displaying lower potential energy and improved structural quality as assessed by MolProbity metrics.SIGNIFICANCEWe propose a novel framework to directly incorporate forces from molecular dynamics as gradients for training deep learning models like AlphaFold2. We implement our method as a PyTorch loss function, OpenMM-Loss, which frames the potential energy of predicted protein structures as a minimization objective. When applied to OpenFold, our method demonstrates improved structural quality, lower potential energy, and comparable accuracy relative to the OpenFold baseline. Our framework may enhance the ability of deep learning models to recapitulate fundamental biophysical principles, reducing the number of structural irregularities in their predictions and paving the way for more effective downstream applications.
The Bias of Using Cross-Validation in Genomic Predictions and Its Correction
Authors: Qian Yanzhao , Wang Dinghao , Ding Qi Xuan , Greenberg Matthew , Long Quan 
Published: October 2023

AbstractCross-validation (CV) is a widely used technique in statistical learning for model evaluation and selection. Meanwhile, various of statistical learning methods, such as Generalized Least Square (GLS), Linear Mixed-Effects Models (LMM), and regularization methods are commonly used in genomic predictions, a field that utilizes DNA polymorphisms to predict phenotypic traits. However, due to high dimensionality, relatively small sample sizes, and data sparsity in genomic data, CV in these scenarios may lead to an underestimation of the generalization error. In this work, we analyzed the bias of CV in eight methods: Ordinary Least Square (OLS), GLS, LMM, Lasso, Ridge, elastic-net (ENET), and two hybrid methods: one combining GLS with Ridge regularization (GLS+Ridge), and the other combining LMM with Ridge regularization (LMM+Ridge). Leveraging genomics data from the 1,000 Genomes Project and simulated phenotypes, our investigation revealed the presence of bias in all these methods. To address this bias, we adapted a variance-structure method known as Cross-Validation Correction (CVc). This approach aims to rectify the cross-validation error by providing a more accurate estimate of the generalization error. To quantify the performance of our adapted CVc towards all these methods, we applied the trained model to an independently generated dataset, which served as a gold standard for validating the models and calculating the generalization error. The outcomes show that, by leveraging CVc, we corrected the CV bias for most of the methods mentioned above, with two exceptions that are unrectifiable methods: ENET and Lasso. Our work revealed the substantial bias in the use of CV in genomics, a phenomenon under-appreciated by the field of statistical genomics and medicine. Additionally, we demonstrated that bias-corrected models may be formed by adapting CVc, although more work is needed to cover the full spectrum.