bluuebunny/crossref_metadata_2025_split huggingface dataset:
A High-Throughput Microphysiological Liver Chip System to Model Direct and Idiosyncratic Drug-Induced Liver Injury Using Human Liver Organoids
Authors: Zhang Charles J. , Meyer Sophia R. , Garcia Max A. , Procario Megan C. , Yoo Sanghee , Jolly Amber L. , Kim Sumin , Kim Jiho , Baek Kyusuk , Kersten Roland D. , Fontana Robert J. , Sexton Jonathan Z. 
Published: December 2023

1.AbstractObjectiveDrug-induced liver injury (DILI) is a major failure mode in pharmaceutical development. This study aims to address the limitations of existing preclinical models by introducing a high-throughput, microfluidic liver-on-a-chip system, termed as “Curio Barrier Liver Chips,” seeded with human liver organoids to enable metabolic and phenotypic morphologic characterization.MethodsCurio Barrier liver chips, fabricated in an 8×2 well configuration, were utilized to establish 3D liver organoid cultures. Human-induced pluripotent stem cells (iPSCs) were differentiated into hepatic-like organoids (HLOs), and their viability, liver-specific functions, and pharmacological responses were assessed over 28 days.ResultsThe Curiochips successfully maintained liver physiology and function, showing strong albumin secretion and cytochrome P450 (CYP) activities through 28 days. Unlike traditional models requiring millimolar drug concentrations to detect hepatotoxicity, this platform showed increased sensitivity for APAP and FIAU at micromolar concentrations.In situdifferentiation of foregut spheroids to liver organoids was also achieved, further simplifying the establishment of liver chips. Furthermore, the chips demonstrated viability, function and DILI responsiveness for 28 days making this an improved model for studying idiosyncratic DILI with prolonged drug exposure and high-throughput capabilities compared to other available systems or primary human hepatocytes.ConclusionsThe Curiochips offer an advanced, miniaturizedin vitromodel for early-stage drug development and a sensitive, responsive and cost-effective means to detect direct hepatotoxicity. The high-throughput capability, coupled with robust functionality and pharmacological responses make it a promising tool for improving the prediction and understanding of DILI mechanisms in general and those that required prolonged drug exposure. The model also opens new avenues for research in other chronic liver diseases.
An inducible genetic tool for tracking and manipulating specific microglial states in development and disease
Authors: Barclay Kia M. , Abduljawad Nora , Cheng Zuolin , Kim Min Woo , Zhou Lu , Yang Jin , Rustenhoven Justin , Mazzitelli Perez Jose , Smyth Leon , Beatty Wandy , Hou JinChao , Saligrama Naresha , Colonna Marco , Yu Guoqiang , Kipnis Jonathan , Li Qingyun 
Published: December 2023

SUMMARYRecent single-cell RNA sequencing studies have revealed distinct microglial states in development and disease. These include proliferative region-associated microglia (PAM) in developing white matter and disease-associated microglia (DAM) prevalent in various neurodegenerative conditions. PAM and DAM share a similar core gene signature and other functional properties. However, the extent of the dynamism and plasticity of these microglial states, as well as their functional significance, remains elusive, partly due to the lack of specific tools. Here, we report the generation of an inducible Cre driver line, Clec7a-CreERT2, designed to target PAM and DAM in the brain parenchyma. Utilizing this tool, we profile labeled cells during development and in several disease models, uncovering convergence and context-dependent differences in PAM/DAM gene expression. Through long-term tracking, we demonstrate surprising levels of plasticity in these microglial states. Lastly, we specifically depleted DAM in cuprizone-induced demyelination, revealing their roles in disease progression and recovery.
A ketogenic diet reduces age-induced chronic neuroinflammation in mice
Authors: Nomura Mitsunori , Murad Natalia Faraj , Madhavan Sidharth S , Eap Brenda , Garcia Thelma Y , Aguirre Carlos Galicia , Verdin Eric , Ellerby Lisa , Furman David , Newman John C 
Published: December 2023

AbstractBeta-hydroxybutyrate (BHB) is a ketone body synthesized during fasting or strenuous exercise. Our previous study demonstrated that a cyclic ketogenic diet (KD), which induces BHB levels similar to fasting every other week, reduces midlife mortality and improves memory in aging mice. BHB actively regulates gene expression and inflammatory activation through non-energetic signaling pathways. Neither of these activities has been well-characterized in the brain and they may represent mechanisms by which BHB affects brain function during aging. First, we analyzed hepatic gene expression in an aging KD-treated mouse cohort using bulk RNA-seq. In addition to the downregulation of TOR pathway activity, cyclic KD reduces inflammatory gene expression in the liver. We observed via flow cytometry that KD also modulates age-related systemic T cell functions. Next, we investigated whether BHB affects brain cells transcriptionallyin vitro. Gene expression analysis in primary human brain cells (microglia, astrocytes, neurons) using RNA-seq shows that BHB causes a mild level of inflammation in all three cell types. However, BHB inhibits the more pronounced LPS-induced inflammatory gene activation in microglia. Furthermore, we confirmed that BHB similarly reduces LPS-induced inflammation in primary mouse microglia and bone marrow-derived macrophages (BMDMs). BHB is recognized as an inhibitor of histone deacetylase (HDAC), an inhibitor of NLRP3 inflammasome, and an agonist of the GPCR Hcar2. Nevertheless, in microglia, BHB’s anti-inflammatory effects are independent of these known mechanisms. Finally, we examined the brain gene expression of 12-month-old male mice fed with one-week and one-year cyclic KD. While a one-week KD increases inflammatory signaling, a one-year cyclic KD reduces neuroinflammation induced by aging. In summary, our findings demon-strate that BHB mitigates the microglial response to inflammatory stimuli, like LPS, possibly leading to decreased chronic inflammation in the brain after long-term KD treatment in aging mice.
Ecological specialization and local adaptation in sympatric sexual and asexual grass thrips species
Authors: Ghali Karim , van der Kooi Casper J. , Ramella Elodie , Henry Morgane , Schwander Tanja 
Published: December 2023

AbstractThe maintenance of sex is difficult to explain in the face of the demographic advantages of asexuality, especially when sexual and asexual lineages co-occur and compete. Here, we test if niche divergence and specialization can contribute to the maintenance of sympatric populations of two closely related, sexual and asexualAptinothripsgrass thrips species. In mesocosm experiments, ecological niches and ecological specialization were inferred from thrips performances on different grass species used as hosts in natural populations. Sexual and asexual thrips performed best on different grass hosts, indicating niche differentiation. The asexual species was also characterized by a broader fundamental ecological niche than the sexual one. However, niche differentiation is unlikely to explain the maintenance of the two species in sympatry because the reproductive rate of asexual females generally exceeded that of sexual ones. Surprisingly, the asexual but not sexual species showed geographic variation in the fundamental niche. This geographic variation likely stems from different clonal assemblages at different locations because different asexual genotypes have different ecological niches. Across natural populations, the performance of asexual females on a specific grass species was further positively correlated with the frequency of that grass species, consistent with adaptation to locally abundant grasses. Altogether, our results suggest that niche differentiation contributes little to the maintenance of grass thrips species with different reproductive modes and that asexuality facilitates adaptation to a diversity of co-occurring host plants.
An IL-2 mutein increases IL-10 and CTLA-4-dependent suppression of dendritic cells by regulatory T cells
Authors: Jamison Braxton L. , Lawrance Matthew , Wang Chun Jing , DeBerg Hannah A. , Sansom David M. , Gavin Marc A. , Walker Lucy S.K. , Campbell Daniel J. 
Published: December 2023

SummaryInterleukin-2 (IL-2) variants with increased CD25 dependence that selectively expand Foxp3+regulatory T (TR) cells are in clinical trials for treating inflammatory diseases. Using an Fc-fused IL-2 mutein (Fc.IL-2 mutein) we developed that prevents diabetes in non-obese diabetic (NOD) mice, we show that Fc.IL-2 mutein induced an activated TRpopulation with elevated proliferation, a transcriptional program associated with Stat5- and TCR-dependent gene modules, and high IL-10 and CTLA-4 expression. Increased IL-10 signaling limited surface MHC class II upregulation during conventional dendritic cell (cDC) maturation, while increased CTLA-4-dependent transendocytosis led to the transfer of CD80 and CD86 costimulatory ligands from maturing cDCs to TRcells. In NOD mice, Fc.IL-2 mutein treatment promoted the suppression of cDCs in the inflamed pancreas and pancreatic lymph nodes resulting in T cell anergy. Thus, IL-2 mutein-expanded TRcells have enhanced functional properties and restrict cDC function, offering promise for targeted immunotherapy use in autoimmune disease.
External validation of machine learning models - registered models and adaptive sample splitting
Authors: Gallitto Giuseppe , Englert Robert , Kincses Balint , Kotikalapudi Raviteja , Li Jialin , Hoffschlag Kevin , Bingel Ulrike , Spisak Tamas 
Published: December 2023

AbstractMultivariate predictive models play a crucial role in enhancing our understanding of complex biological systems and in developing innovative, replicable tools for translational medical research. However, the complexity of machine learning methods and extensive data pre-processing and feature engineering pipelines can lead to overfitting and poor generalizability. An unbiased evaluation of predictive models necessitates external validation, which involves testing the finalized model on independent data. Despite its importance, external validation is often neglected in practice due to the associated costs. Here we propose that, for maximal credibility, model discovery and external validation should be separated by the public disclosure (e.g. pre-registration) of feature processing steps and model weights. Furthermore, we introduce a novel approach to optimize the trade-off between efforts spent on training and external validation in such studies. We show on data involving more than 3000 participants from four different datasets that, for any “sample size budget”, the proposed adaptive splitting approach can successfully identify the optimal time to stop model discovery so that predictive performance is maximized without risking a low powered, and thus inconclusive, external validation. The proposed design and splitting approach (implemented in the Python package “AdaptiveSplit”) may contribute to addressing issues of replicability, effect size inflation and generalizability in predictive modeling studies.
Experimental structures of antibody/MHC-I complexes reveal details of epitopes overlooked by computational prediction
Authors: Boyd Lisa F. , Jiang Jiansheng , Ahmad Javeed , Natarajan Kannan , Margulies David H. 
Published: December 2023

ABSTRACTMonoclonal antibodies (mAb) to major histocompatibility complex class I (MHC-I) molecules have proved to be crucial reagents for tissue typing and fundamental studies of immune recognition. To augment our understanding of epitopic sites seen by a set of anti-MHC-I mAb, we determined X-ray crystal structures of four complexes of anti-MHC-I antigen-binding fragments (Fab) bound to peptide/MHC-I/β2m (pMHC-I). An anti-H2-DdmAb, two anti-MHC-I α3 domain mAb, and an anti-β2-microglobulin (β2m) mAb bind pMHC-I at sites consistent with earlier mutational and functional experiments, and the structures explain allelomorph specificity. Comparison of the experimentally determined structures with computationally derived models using AlphaFold Multimer (AF-M) showed that although predictions of the individual pMHC-I heterodimers were quite acceptable, the computational models failed to properly identify the docking sites of the mAb on pMHC-I. The experimental and predicted structures provide insight into strengths and weaknesses of purely computational approaches and suggest areas that merit additional attention.ONE SENTENCE SUMMARYX-ray structures of anti-MHC-I/MHC-I complexes define epitopes overlooked by computational prediction.
Intermediate CA1 is Required for Object-in-Place Recognition Memory in Mice
Authors: Cruz-Sanchez Arely , Inayat Mehreen , Parikh Parjanya , Appings Ryan , Violi Francesca , Arruda-Carvalho Maithe 
Published: December 2023

AbstractMany behaviors that are essential for survival, such as retrieving food, finding shelter and locating predator cues, rely on forming effective associations between the identity and location of spatial elements. This identity-location association is commonly assessed in rodents using spontaneous object-in-place (OiP) recognition memory tasks. OiP recognition memory deficits are seen in autism spectrum disorder, schizophrenia, and are used to detect early onset of Alzheimer’s disease. These deficits are replicated in animal models of neurodevelopmental, neurodegenerative and chromosomal disorders. Mouse models have been widely adopted in behavioral and systems neuroscience research for their ease of genetic manipulations, and yet very few studies have successfully assessed OiP recognition memory or its neural correlates in mice. To address this limitation, we first established that adult C57/129J and C57BL/6J male and female mice are able to successfully perform the two-object, but not the four-object version of the spontaneous OiP recognition task, with retention intervals of five minutes and one hour. Next, using chemogenetic inhibition, we found that two-object OiP requires the activity of the intermediate CA1 (iCA1) subregion of the hippocampus, but not the medial prefrontal cortex or iCA1-medial prefrontal cortex connections. Our data identify hippocampal subregion specialization in the successful assessment of OiP recognition memory in mice, expanding our understanding of the neural basis of spatial memory processing.Significance StatementAssociations between the identity and location of spatial elements (what-where associations), underlie essential behaviours such as finding food, locating shelter and safely navigating the environment. Deficits in identity-location processing occur in patients with neurodevelopmental and neurodegenerative disorders, and are replicated in rodent models using object-in-place (OiP) recognition tasks. While mice have emerged as a widely used animal model to study the biological mechanisms underlying these disorders, nothing is known about the neural substrates of OiP memory in mice. Here we have established and validated a robust experimental paradigm to assess OiP memory in mice, uncovering a specialized contribution of the hippocampal subregion intermediate CA1 to OiP performance and deepening our understanding of the neural signatures of spatial memory processing.
Contextualized Networks Reveal Heterogeneous Transcriptomic Regulation in Tumors at Sample-Specific Resolution
Authors: Ellington Caleb N. , Lengerich Benjamin J. , Watkins Thomas B.K. , Yang Jiekun , Xiao Hanxi , Kellis Manolis , Xing Eric P. 
Published: December 2023

AbstractCancers are shaped by somatic mutations, microenvironment, and patient background, each altering gene expression and regulation in complex ways, resulting in heterogeneous cellular states and dynamics. Inferring gene regulatory network (GRN) models from expression data can help characterize this regulation-driven heterogeneity, but network inference requires many statistical samples, traditionally limiting GRNs to cluster-level analyses that ignore intra-cluster heterogeneity. We propose to move beyond cluster-based analyses by usingcontextualizedlearning, a multi-task learning paradigm which allows us to infer sample-specific models using phenotypic, molecular, and environmental information pertinent to the model, encoded as the model’s “context” to be conditioned on. We unify three network model classes (Correlation, Markov, Neighborhood) and estimate context-specific GRNs for 7997 tumors across 25 tumor types, with each network contextualized by copy number and driver mutation profiles, tumor microenvironment, and patient demographics. Contextualized GRNs provide a structured view of expression dynamics at sample-specific resolution, which reveal co-expression modules in correlation networks (CNs), as well as cliques and independent regulatory elements in Markov Networks (MNs) and Neighborhood Regression Networks (NNs). Our generative modeling approach allows us to predict GRNs for unseen tumor types based on a pan-cancer model of how somatic mutations affect gene regulation. Finally, contextualized networks enable GRN-based precision oncology, explaining known biomarkers in terms of network-mediated effects, and leading to novel subtypings for thyroid, brain, and gastrointestinal tumors that improve survival prognosis.
Evaluating polarizable biomembrane simulations against experiments
Authors: Antila Hanne S. , Dixit Sneha , Kav Batuhan , Madsen Jesper J. , Miettinen Markus S. , Ollila O. H. Samuli 
Published: December 2023

AbstractOwing to the increase of available computational capabilities and the potential for providing more accurate description, polarizable molecular dynamics force fields are gaining popularity in modelling biomolecular systems. It is, however, crucial to evaluate how much precision is truly gained with the increased cost and complexity of the simulation. Here, we leverage the NMRlipids open collaboration and Databank to assess the performance of available polarizable lipid models—the CHARMM-Drude and the AMOEBA-based parameters—against high-fidelity experimental data and compare them to the top-performing non-polarizable models. While some improvement in the description of ion binding to membranes is observed in the most recent CHARMM-Drude parameters, and the conformational dynamics of AMOEBA-based parameters are excellent, the best non-polarizable models tend to outperform their polarizable counterparts for each property we explored. The identified shortcomings range from inaccuracies in describing the conformational space of lipids to excessively slow conformational dynamics. Our results provide valuable insights for further refinement of polarizable lipid force fields and for selecting the best simulation parameters for specific applications.
Distinct mechanisms drive plant-nitrifier interactions in topsoil and subsoil
Authors: Liang Di , Ji Niuniu , Kent Angela , Yang Wendy H. 
Published: December 2023

AbstractPlants can influence soil microbes through resource acquisition and interference competition, with consequences for ecosystem function such as nitrification. However, how plants alter soil conditions to influence nitrifiers and nitrification rates remains poorly understood, especially in the subsoil. Here, coupling the15N isotopic pool dilution technique, high throughput sequencing andin situsoil O2monitoring, we investigated how a deep-rooted perennial grass, miscanthus, versus an adjacent shallow-rooted turfgrass reference shapes nitrifier assembly and function along 1 meter soil profiles. In topsoil, the suppression of ammonia (NH3) oxidizing archaea (AOA) and gross nitrification rates in miscanthus relative to the reference likely resulted from nitrifiers being outcompeted by plant roots and heterotrophic bacteria for ammonium (NH4+). The stronger tripartite competition under miscanthus may have been caused in part by the lower soil organic matter (SOM) content, which supported lower gross nitrogen (N) mineralization, the major soil process that produces NH4+. In contrast, below 10 cm soil depth, significantly greater gross nitrification rates were observed in miscanthus compared to the reference. This was likely driven by the significantly lower oxygen (O2) in miscanthus than reference subsoil, which selected against aerobic heterotrophic bacteria but in favor of AOA. Overall, we found that plants can regulate AOA community structure and function through different mechanisms in topsoil and subsoil, with suppression of nitrification in topsoil and enhancement of nitrification in subsoil.
HiCDiff: single-cell Hi-C data denoising with diffusion models
Authors: Wang Yanli , Cheng Jianlin 
Published: December 2023

AbstractThe genome-wide single-cell chromosome conformation capture technique, i.e., single-cell Hi-C (ScHi-C), was recently developed to interrogate the conformation of the genome of individual cells. However, single-cell Hi-C data are much sparser and noisier than bulk Hi-C data of a population of cells, making it difficult to apply and analyze them in biological research. Here, we developed the first generative diffusion models (HiCDiff) to denoise single-cell Hi-C data in the form of chromosomal contact matrices. HiCDiff uses a deep residual network to remove the noise in the reverse process of diffusion and can be trained in both unsupervised and supervised learning modes. Benchmarked on several single-cell Hi-C test datasets, the diffusion models substantially remove the noise in single-cell Hi-C data. The unsupervised HiCDiff outperforms most supervised non-diffusion deep learning methods and achieves the performance comparable to the state-of-the-art supervised deep learning method in terms of multiple metrics, demonstrating that diffusion models are a useful approach to denoising single-cell Hi-C data. Moreover, its good performance holds on denoising bulk Hi-C data.