bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cell age drives asynchronous transcriptome aging
Authors: Yang Ming , Harrison Benjamin R. , Promislow Daniel E.L. 
Published: June 2023

AbstractOrgans age at different rates within a single individual. Such asynchrony in aging has been widely observed at multiple levels, from functional hallmarks, such as anatomical structures and physiological processes, to molecular endophenotypes, such as the transcriptome and metabolome. However, we lack a conceptual framework to understand why some components age faster than others. Just as demographic models explain why aging evolves, here we test the hypothesis that demographic differences among cell types, determined by cell-specific differences in turnover rate, can explain why the transcriptome shows signs of aging in some cell types but not others. Through analysis of mouse single-cell transcriptome data across diverse organs and ages, we find that cellular age explains a large proportion of the variation in the age-related increase in transcriptome variance. We further show that long-lived cells are characterized by relatively high expression of genes associated with proteostasis, and that the transcriptome of long-lived cells shows greater evolutionary constraint than short-lived cells. In contrast, in short-lived cell types the transcriptome is enriched for genes associated with DNA repair. Based on these observations, we develop a novel heuristic model that explains how and why aging rates differ among cell types.
Intestinal epithelial adaptations to vertical sleeve gastrectomy defined at single-cell resolution
Authors: Koch-Laskowski Kieran , Kim Ki-Suk , Bethea Maigen , Fuller Kelly N. Z. , Sandoval Darleen A. , Sethupathy Praveen 
Published: June 2023

SummaryThe gut plays a key role in regulating metabolic health. Dietary factors disrupt intestinal physiology and contribute to obesity and diabetes, whereas bariatric procedures such as vertical sleeve gastrectomy (VSG) cause gut adaptations that induce robust metabolic improvements. However, our understanding of these adaptations at the cellular and molecular levels remains limited. In a validated murine model, we leverage single-cell transcriptomics to determine how VSG impacts different cell lineages of the small intestinal epithelium. We define cell type-specific genes and pathways that VSG rescues from high-fat diet perturbation and characterize additional rescue-independent changes brought about by VSG. We show that Paneth cells have increased expression of the gut peptide Reg3g after VSG. We also find that VSG restores pathways pertaining to mitochondrial respiration and cellular metabolism, especially within crypt-based cells. Overall, our study provides unprecedented molecular resolution of VSG’s therapeutic effects on the gut epithelium.
Harnessing Deep Learning to Analyze Cryptic Morphological Variability ofMarchantia polymorpha
Authors: Tomizawa Yoko , Minamino Naoki , Shimokawa Eita , Kawamura Shogo , Komatsu Aino , Hiwatashi Takuma , Nishihama Ryuichi , Ueda Takashi , Kohchi Takayuki , Kondo Yohei 
Published: June 2023

AbstractCharacterizing phenotypes is a fundamental aspect of biological sciences, although it can be challenging due to various factors. For instance, the liverwort (Marchantia polymorpha), a model system for plant biology, exhibits morphological variability, making it difficult to identify and quantify distinct phenotypic features using objective measures. To address this issue, we utilized a deep learning-based image classifier that can handle plant images directly without manual extraction of phenotypic features, and analyzed bright-field images ofM. polymorpha. This dioicous plant species exhibits morphological differences between male and female wild accessions at an early stage of gemmaling growth, although it remains elusive whether the differences are attributable to sexual dimorphism or autosomal genetic variation. To dissect the genomic factors, we established a male and female set of recombinant inbred lines (RILs) from a set of male and female wild accessions. We then trained deep-learning models to classify the sexes of the RILs and the wild accessions. Our results showed that the trained classifiers accurately classified male and female gemmalings of wild accessions in the first week of growth, confirming the intuition of plant researchers in a reproducible and objective manner. In contrast, the RILs were less distinguishable, indicating that the differences between the parental wild accessions arose from autosomal variations instead of sexual dimorphism. Furthermore, we validated our trained models by an “explainable AI” technique that highlights image regions relevant to the classification. Our findings demonstrate that the classifier-based approach provides a powerful tool for analyzing plant species that lack standardized phenotyping metrics.
Comparing neural language models for medical concept representation and patient trajectory prediction
Authors: Bornet Alban , Proios Dimitrios , Yazdani Anthony , Santero Fernando Jaume , Haller Guy , Choi Edward , Teodoro Douglas 
Published: June 2023

AbstractEffective representation of medical concepts is crucial for secondary analyses of electronic health records. Neural language models have shown promise in automatically deriving medical concept representations from clinical data. However, the comparative performance of different language models for creating these empirical representations, and the extent to which they encode medical semantics, has not been extensively studied. This study aims to address this gap by evaluating the effectiveness of three popular language models – word2vec, fastText, and GloVe – in creating medical concept embeddings that capture their semantic meaning. By using a large dataset of digital health records, we created patient trajectories and used them to train the language models. We then assessed the ability of the learned embeddings to encode semantics through an explicit comparison with biomedical terminologies, and implicitly by predicting patient outcomes and trajectories with different levels of available information. Our qualitative analysis shows that empirical clusters of embeddings learned by fastText exhibit the highest similarity with theoretical clustering patterns obtained from biomedical terminologies, with a similarity score between empirical and theoretical clusters of 0.88, 0.80, and 0.92 for diagnosis, procedure, and medication codes, respectively. Conversely, for outcome prediction, word2vec and GloVe tend to outperform fastText, with the former achieving AUROC as high as 0.78, 0.62, and 0.85 for length-of-stay, readmission, and mortality prediction, respectively. In predicting medical codes in patient trajectories, GloVe achieves the highest performance for diagnosis and medication codes (AUPRC of 0.45 and of 0.81, respectively) at the highest level of the semantic hierarchy, while fastText outperforms the other models for procedure codes (AUPRC of 0.66). Our study demonstrates that subword information is crucial for learning medical concept representations, but global embedding vectors are better suited for more high-level downstream tasks, such as trajectory prediction. Thus, these models can be harnessed to learn representations that convey clinical meaning, and our insights highlight the potential of using machine learning techniques to semantically encode medical data.
Cell-type-specific Alzheimer’s disease polygenic risk scores are associated with distinct disease processes in Alzheimer’s disease
Authors: Yang Hyun-Sik , Teng Ling , Kang Daniel , Menon Vilas , Ge Tian , Finucane Hilary K. , Schultz Aaron P. , Properzi Michael , Klein Hans-Ulrich , Chibnik Lori B. , Schneider Julie A. , Bennett David A. , Hohman Timothy J. , Mayeux Richard P. , Johnson Keith A. , De Jager Philip L. , Sperling Reisa A. 
Published: June 2023

AbstractAlzheimer’s disease (AD) heritability is enriched in glial genes, but how and when cell-type-specific genetic risk contributes to AD remains unclear. Here, we derive cell-type-specific AD polygenic risk scores (ADPRS) from two extensively characterized datasets. In an autopsy dataset spanning all stages of AD (n=1,457), astrocytic (Ast) ADPRS was associated with both diffuse and neuritic Aβ plaques, while microglial (Mic) ADPRS was associated with neuritic Aβ plaques, microglial activation, tau, and cognitive decline. Causal modeling analyses further clarified these relationships. In an independent neuroimaging dataset of cognitively unimpaired elderly (n=2,921), Ast-ADPRS were associated with Aβ, and Mic-ADPRS was associated with Aβ and tau, showing a consistent pattern with the autopsy dataset. Oligodendrocytic and excitatory neuronal ADPRSs were associated with tau, but only in the autopsy dataset including symptomatic AD cases. Together, our study provides human genetic evidence implicating multiple glial cell types in AD pathophysiology, starting from the preclinical stage.
Revealing the Organ-Specific Expression ofSPTBN1using Single-Cell RNA Sequencing Analysis
Authors: Jung Jongyun , Wu Qing 
Published: June 2023

AbstractDespite the recent technological advances in single-cell RNA sequencing, it is still unknown how three marker genes (SPTBN1,EPDR1, andPKDCC), which are associated with bone fractures and highly expressed in the muscle tissue, are contributing to the development of other tissues and organs at the cellular level. This study aims to analyze three marker genes at the single-cell level using 15 organ tissue types of adult human cell atlas (AHCA). The single-cell RNA sequencing analysis used three marker genes and a publicly available AHCA data set. AHCA data set contains more than 84,000 cells from 15 organ tissue types. Quality control filtering, dimensionality reduction, clustering for cells, and data visualization were performed using the Seurat package. A total of 15 organ types are included in the downloaded data sets: Bladder, Blood, Common Bile Duct, Esophagus, Heart, Liver, Lymph Node, Marrow, Muscle, Rectum, Skin, Small Intestine, Spleen, Stomach, and Trachea. In total, 84,363 cells and 228,508 genes were included in the integrated analysis. A marker gene ofSPTBN1is highly expressed across all 15 organ types, particularly in the Fibroblasts, Smooth muscle cells, and Tissue stem cells of the Bladder, Esophagus, Heart, Muscle, Rectum, Skin, and Trachea. In contrast,EPDR1is highly expressed in the Muscle, Heart, and Trachea, andPKDCCis only expressed in Heart. In conclusion,SPTBN1is an essential protein gene in physiological development and plays a critical role in the high expression of fibroblasts in multiple organ types. TargetingSPTBN1may prove beneficial for fracture healing and drug discovery.Author SummaryThree marker genes (SPTBN1,EPDR1, andPKDCC) are playing a critical role in the shared genetic mechanisms between bone and muscle. However, how these marker genes contribute to developing other tissues and organs at the cellular level is unknown. Here, we build on prior work to study an unappreciated degree of heterogeneity of three marker genes in 15 adult human organs by using single-cell RNA sequencing technology. Our analysis included 15 organ types: Bladder, Blood, Common Bile Duct, Esophagus, Heart, Liver, Lymph Node, Marrow, Muscle, Rectum, Skin, Small Intestine, Spleen, Stomach, and Trachea. In total, 84,363 cells from 15 different organ types were included. In all 15 organ types,SPTBN1is highly expressed, including fibroblasts, smooth muscle cells, and skin stem cells of the bladder, esophagus, heart, muscles, and rectum. The first-time discovery of the high expression ofSPTBN1in 15 organ types suggests that it may play a critical role in physiological development. Our study concludes that targetingSPTBN1may benefit fracture healing and drug discovery.
Evolution and diversity of biomineralized columnar architecture in early Cambrian phosphatic-shelled brachiopods
Authors: Zhang Zhiliang , Zhang Zhifei , Holmer Lars E. , Topper Timothy P. , Pan Bing , Li Guoxiang 
Published: June 2023

AbstractBiologically-controlled mineralization producing organic-inorganic composites (hard skeletons) by metazoan biomineralizers has been an evolutionary innovation since the earliest Cambrian. Among them, linguliform brachiopods are one of the key invertebrates that secrete calcium phosphate minerals to build their shells. One of the most distinct shell structures is the organo-phosphatic cylindrical column exclusive to phosphatic-shelled brachiopods, including both crown and stem groups. However, the complexity, diversity and biomineralization processes of these microscopic columns are far from clear in brachiopod ancestors. Here, exquisitely well-preserved columnar shell ultrastructures are reported for the first time in the earliest eoobolidsLatusobolus xiaoyangbaensisgen. et sp. nov. andEoobolus acutulussp. nov from the Cambrian Series 2 Shuijingtuo Formation of South China. The hierarchical shell architectures, epithelial cell moulds, and the shape and size of cylindrical columns are scrutinised in these new species. Their calcium phosphate-based biomineralized shells are mainly composed of stacked sandwich columnar units. The secretion and construction of the stacked sandwich model of columnar architecture, which played a significant role in the evolution of linguliforms, is highly biologically controlled and organic-matrix mediated. Furthermore, a continuous transformation of anatomic features resulting from the growth of diverse columnar shells is revealed between Eoobolidae, Lingulellotretidae and Acrotretida, shedding new light on the evolutionary growth and adaptive innovation of biomineralized columnar architecture among early phosphatic-shelled brachiopods during the Cambrian explosion.
Assessing the predictive ability of computational epitope prediction methods on Fel d 1 and other allergens
Authors: Kwon Hyeji , Ko Soobon , Ha Kyungsoo , Lee Jungjoon K. , Choi Yoonjoo 
Published: June 2023

AbstractWhile computational epitope prediction methods have found broad application, their use, specifically in allergy-related contexts, remains relatively less explored. This study benchmarks several publicly available epitope prediction tools, focusing on the allergenic IgE and T-cell epitopes of Fel d 1, an extensively studied allergen. Using a variety of tools accessible via the Immune Epitope Database (IEDB) and other resources, we evaluate their ability to identify the known linear IgE and T-cell epitopes of Fel d 1. Our results show a limited effectiveness for B-cell epitope prediction methods, with most performing only marginally better than random selection. We also explored the general predictive abilities on other allergens, and the results were largely random. When predicting T-cell epitopes, ProPred successfully identified all known Fel d 1 T-cell epitopes, whereas the IEDB approach missed two known epitopes and demonstrated a tendency to over-predict. However, when applied to a larger test set, both methods performed only slightly better than random selection. Our findings show the limitations of current computational epitope prediction methods in accurately identifying allergenic epitopes, emphasizing the need for methodological advancements in allergen research.
IL-1R signaling drives enteric glia-macrophage interactions in colorectal cancer
Authors: van Baarle Lies , Simone Veronica De , Schneider Linda , Santhosh Sneha , Abdurahiman Saeed , Biscu Francesca , Schneider Reiner , Zanoletti Lisa , Verbandt Sara , Hu Zedong , Stakenborg Michelle , Ke Bo-Jun , García-Reyes Balbina , Henn Jonas , Toma Marieta , Vanmechelen Maxime , Smet Frederik De , Ibiza Sales , Tejpar Sabine , Wehner Sven , Matteoli Gianluca 
Published: June 2023

ABSTRACTEnteric glial cells (EGCs) have been implicated in colorectal cancer (CRC) pathogenesis. However, their precise mechanisms of interaction with the CRC immune cell compartment and pro-tumorigenic role remain unclear. This study aimed to investigate the immunomodulatory effects of EGCs on tumor-associated macrophages (TAMs) and their involvement in CRC progression. Using EGC depletion and supplementation models, we assessed the impact of EGCs on the immunomodulation of orthotopic murine CRC. Furthermore, by making use of Bulk RNA-sequencing of CRC EGCs and single-cell sequencing of the tumor microenvironment, we identified the factors involved in the EGC-TAM crosstalk. Findings demonstrate that EGCs acquire a reactive and immunomodulatory phenotype in both murine CRC models and patients, influencing TAM differentiation. Mechanistically, secretion of IL-1 by tumor-infiltrating monocytes and macrophages triggers the phenotypic and functional switch of CRC EGCs via IL-1R. Consequently, tumor EGCs secrete IL-6, promoting the differentiation of monocytes into pro-tumorigenic SPP1+TAMs. Importantly, the reactive tumor EGCs phenotype correlates with worse disease outcomes in preclinical models and CRC patients. Here we uncover a previously unexplored neuroimmune interaction between EGCs and TAMs within the colorectal tumor microenvironment, informing potential therapeutic strategies and enhancing our understanding of CRC progression.eTOC SummaryOur study unveils a novel neuroimmune interaction between enteric glia and TAMs in colon carcinoma. Monocyte/Macrophage-derived IL-1 activates enteric glia, leading to the differentiation of pro-tumorigenic SPP1+TAMs via glial-derived IL-6. Blocking glial IL-1R-signaling reduces colonic tumor lesions, highlighting IL-1R as a potential therapeutic target.
A novel method for tracking nitrogen kineticsin vivoandex vivousing radioactive nitrogen-13 gas and Positron Emission Tomography
Authors: Ashworth Edward T , Ogawa Ryotaro , Vera David , Lindholm Peter 
Published: June 2023

AbstractRationaleDecompression sickness (DCS) is caused by gaseous nitrogen dissolved in tissues forming bubbles during decompression. To date no method exists to identify nitrogen within tissues, but with advances in PET technology it may be possible to track gaseous radionuclides into tissues. We aimed to develop a method to track nitrogen movementin vivothat could then be used to further our understanding of DCS using nitrogen-13 (13N2) – a radioactive isotope of nitrogen that emits β+ radiation.MethodsA single anesthetized and ventilated Sprague Dawley rat lay supine inside a PET scanner for 30 min. The rat breathed oxygen for the first 2 min, then was switched to a bag containing13N2gas mixed with oxygen for 20 min, then breathed oxygen alone for the final 8 min. Gas samples were drawn from the inspiratory line at 5, 15 and 25 min. The PET scanner recorded13N2with energy windows of 250-750 keV. Following the scan, a mixed blood sample was taken from the heart, while the brain, liver, femur and thigh muscle were removed to determine organ radioactivity using a gamma counter.ResultsThe gas samples at 5 (5.7 kbq.ml-1) and 15 min (5.3 kbq.ml-1) showed radioactivity in the inspired gas that was absent at 25 min (0.1 kbq.ml-1), when the13N2was stopped. The signal intensity in the PET scanner increased from baseline (0.03) to 2-12 min (0.68±0.31), and 12-22 min (0.88±0.06), before reducing slightly from 22-30 min (0.61±0.04). All organs had radioactivity when measured in the gamma counter, with the highest counts in the liver (12593 counts.min-1.g-1) and the lowest in the muscle (2687 counts.min-1.g-1).Principal ConclusionsThis study successfully demonstrated a quantitative 3D imaging method of tracking nitrogen gas through the body bothin vivoandex vivousing PET.
Direct measurement of dynamic attractant gradients reveals breakdown of the Patlak-Keller-Segel chemotaxis model
Authors: Phan Trung V. , Mattingly Henry H. , Vo Lam , Marvin Jonathan S. , Looger Loren L. , Emonet Thierry 
Published: June 2023

ABSTRACTChemotactic bacteria not only navigate chemical gradients, but also shape their environments by consuming and secreting attractants. Investigating how these processes influence the dynamics of bacterial populations has been challenging because of a lack of experimental methods for measuring spatial profiles of chemoattractants in real time. Here, we use a fluorescent sensor for aspartate to directly measure bacterially generated chemoattractant gradients during collective migration. Our measurements show that the standard Patlak-Keller-Segel model for collective chemotactic bacterial migration breaks down at high cell densities. To address this, we propose modifications to the model that consider the impact of cell density on bacterial chemotaxis and attractant consumption. With these changes, the model explains our experimental data across all cell densities, offering new insight into chemotactic dynamics. Our findings highlight the significance of considering cell density effects on bacterial behavior, and the potential for fluorescent metabolite sensors to shed light on the complex emergent dynamics of bacterial communities.SIGNIFICANCE STATEMENTDuring collective cellular processes, cells often dynamically shape and respond to their chemical environments. Our understanding of these processes is limited by the ability to measure these chemical profiles in real time. For example, the Patlak-Keller-Segel model has widely been used to describe collective chemotaxis towards self-generated gradients in various systems, albeit without direct verification. Here we used a biocompatible fluorescent protein sensor to directly observe attractant gradients created and chased by collectively-migrating bacteria. Doing so uncovered limitations of the standard chemotaxis model at high cell densities and allowed us to establish an improved model. Our work demonstrates the potential for fluorescent protein sensors to measure the spatiotemporal dynamics of chemical environments in cellular communities.
Molecular Dynamics and Water site bias docking method allows the identification of key amino acids in the Carbohydrate Recognition Domain of a viral protein
Authors: Gamarra Marcelo D. , Dieterle Maria Eugenia , Capurro Juan I. Blanco , Radusky Leandro , Piuri Mariana , Modenutti Carlos P. 
Published: June 2023

AbstractCarbohydrate-binding modules (CBMs) are protein domains that typically reside near catalytic domains, increasing substrate-protein proximity by constraining the conformational space of carbohydrates. Due to the flexibility and variability of glycans, the molecular details of how these protein regions recognize their target molecules are not always fully understood. Computational methods, including molecular docking and molecular dynamics simulations, have been employed to investigate lectin-carbohydrate interactions. In this study, we introduce a novel approach that integrates multiple computational techniques to identify the critical amino acids involved in the interaction between a CBM located at the tip of bacteriophage J-1’s tail and its carbohydrate counterparts. Our results highlight three amino acids that play a significant role in binding, which we confirmed through in vitro experiments. By presenting this approach, we offer an intriguing alternative for pinpointing amino acids that contribute to protein-sugar interactions, leading to a more thorough comprehension of the molecular determinants of lectin-carbohydrate interactions.