bluuebunny/crossref_metadata_2025_split huggingface dataset:
STEP: Spatial Transcriptomics Embedding Procedure for Multi-scale Biological Heterogeneities Revelation in Multiple Samples
Authors: Li Lounan , Li Zhong , Yin Xiao-ming , Xu Xiaojiang 
Published: April 2024

AbstractIn the realm of spatially resolved transcriptomics (SRT) and single-cell RNA sequencing (scRNA-seq), addressing the intricacies of complex tissues, integration across non-contiguous sections, and scalability to diverse data resolutions remain paramount challenges. We introduce STEP (Spatial Transcriptomics Embedding Procedure), a novel foundation AI architecture for SRT data, elucidating the nuanced correspondence between biological heterogeneity and data characteristics. STEP’s innovation lies in its modular architecture, combining a Transformer andβ-VAE based backbone model for capturing transcriptional variations, a novel batch-effect model for correcting inter-sample variations, and a graph convolutional network (GCN)-based spatial model for incorporating spatial context—all tailored to reveal biological heterogeneities with un-precedented fidelity. Notably, STEP effectively scales the newly proposed 10x Visium HD technology for both cell type and spatial domain identifications. STEP also significantly improves the demarcation of liver zones, outstripping existing methodologies in accuracy and biological relevance. Validated against leading benchmark datasets, STEP redefines computational strategies in SRT and scRNA-seq analysis, presenting a scalable and versatile framework to the dissection of complex biological systems.
CircRNA_012164/MicroRNA-9-5p axis mediates cardiac fibrosis in diabetic cardiomyopathy
Authors: Wang Honglin , Feng Biao , Wang Eric , Chakrabarti Subrata 
Published: April 2024

AbstractNoncoding RNAs play a part in many chronic diseases and interact with each other to regulate gene expression. MicroRNA-9-5p (miR9) has been thought to be a potential inhibitor of diabetic cardiomyopathy. Here we examined the role of miR9 in regulating cardiac fibrosis in the context of diabetic cardiomyopathy. We further expanded our studies through investigation of a regulatory circularRNA, circRNA_012164, on the action of miR9. We showed at both thein vivoandin vitrolevel that glucose induced downregulation of miR9 and upregulation of circRNA_012164 resulted in the subsequent upregulation of downstream fibrotic genes. Further, knockdown of circRNA_012164 shows protective effects in cardiac endothelial cells and reverses increased transcription of genes associated with fibrosis and fibroblast proliferation through a regulatory axis with miR9. This study presents a novel regulatory axis involving noncoding RNA that is evidently important in the development of cardiac fibrosis in diabetic cardiomyopathy.
GiardiaIncreases Macrophage Production of the Anti-Inflammatory Cytokine Interleukin-10 in Response to Lipopolysaccharide via Macrophage Galactose Binding Lectin (MGL1)
Authors: Angelova V , Darmadi M , Miskovsky E , Fink M , Menegas S , Wexelblatt H , Singer S 
Published: April 2024

AbstractGiardia duodenalisis an intestinal protozoan parasite, common in low- and middle-income countries. Infection is frequently subclinical, even when it is associated with other pathologies like growth stunting in children. Recent longitudinal cohort studies have foundGiardiamore frequently in patients with milder symptoms and have even suggested thatGiardiareduces rotavirus symptom severity. One potential mechanism for limiting disease severity due to other enteropathogens is the promotion of anti-inflammatory responses that limit pathology. Our lab previously showed thatGiardiareduced production of IL-12 by dendritic cells stimulated with TLR agonists. In this study, we show thatGiardiaincreases the production of the anti-inflammatory cytokine IL-10 by mouse peritoneal macrophages in response to bacterial LPS. This potentiation is specific to IL-10, as no changes were seen in the production of the pro-inflammatory cytokine TNF-□. Moreover, peritoneal macrophages from mice lacking macrophage galactose-binding lectin (MGL1), a pathogen recognition receptor that has been previously shown to bind N-acetylgalactosamine, failed to increase IL-10 production after stimulation withGiardiaand LPS.Giardia’s immunoregulation of the IL-10 response may help us understand the parasite’s role in reducing diarrheal severity.
Cancer cell-derived extracellular matrix promotes differentiation of fibroblasts into cancer-associated fibroblasts
Authors: Yondem Eyup , Pesen-Okvur Devrim 
Published: April 2024

AbstractBreast cancer is the most common cancer and the leading cause of cancer-related mortality in women. In addition to cancer cells, the bulk of a breast tumor comprises a range of stromal cell types, including fibroblasts. Cancer-associated fibroblasts (CAF) are crucial players in the tumor microenvironment; however, the process by which fibroblasts differentiate into CAFs is not fully understood. Extracellular matrix (ECM) is known to modulate cell phenotypes. Decellularized ECM (dECM) is a useful tool for studyingin-vitrocell-ECM interactions. Yet, whether cancer cell-derived ECM (ccECM) has a role in CAF formation is not known. Here, we optimized the culture duration (5 days) and the extraction method (freeze-thaw) for obtaining ccECM. We confirmed the presence of ccECM using coomassie blue staining and scanning electron microscopy. We showed that ccECM contained fibronectin and laminin using immunofluorescence staining. In addition, we showed that the presence of ccECM but not glass surface or TGFβ promoted the initial adhesion of fibroblasts, as expected. Finally, using quantitative immunofluorescence microscopy, we demonstrated that in contrast to fibroblasts cultured on glass surfaces in the presence and absence of TGFβ, fibroblasts cultured on ccECM showed increased expression of CAF markers vimentin (2.8 fold), FAP (3.4 fold) and PDGFR β (1.8 fold), but not FSP1/s100A4. Overall, our results indicate that ccECM promotes the differentiation of fibroblasts into CAFs.
Propofol overcome Tamoxifen resistance in breast cancer by modulating tumorogenesis, immune response and metabolism
Authors: Yin Runyang , Gao Jing , Guo Chunyan , Liu Yang 
Published: April 2024

AbstractAlthough 70% of estrogen receptor (ER)-positive breast cancer patients benefit from Tamoxifen therapy, the developing resistance to Tamoxifen leads to high rates of metastasis and poor prognosis. Propofol, a commonly used anesthetic, has been shown to inhibit the occurrence and development of breast cancer. However, its role in anti-endocrine resistance in ER-positive breast cancer remains unclear. In this study, we found that Propofol significantly promotes cell cycle arrest, induces apoptosis, and inhibits proliferation in Tamoxifen-resistant breast cancer cells. Furthermore, transcriptome sequencing analysis revealed 1065 differentially expressed genes (DEGs) between Propofol-treated Tamoxifen-resistant MCF-7 (MCF7-TR) cells and none-treated MCF7-TR cells. Gene ontology annotation enrichment analysis (GO), Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis (KEGG), and gene set enrichment analysis (GSEA) showed that Propofol affects the expression of genes located in the plasma membrane and cell periphery, mainly regulating signals involved in cell cycle regulation, immune response modulation, and metabolism. Our results provide new insights into the mechanism by which Propofol controls resistance to Tamoxifen in breast cancer progression and offer a theoretical basis for clinical treatment.
Deciphering the Immune Subtypes and Signature Genes: A Novel Approach Towards Diagnosing and Prognosticating Severe Asthma through Interpretable Machine Learning
Authors: Hu Yue , Lin Yating , Peng Bo , Xiang Chunyan , Tang Wei 
Published: April 2024

AbstractAsthma, a pervasive pulmonary disorder, affects countless individuals globally. Characterized by chronic inflammation of the bronchial passages, its symptoms include cough, wheezing, dyspnea, and chest tightness. While many manage their symptoms through pharmaceutical interventions and self-care, a significant subset grapples with severe asthma, posing therapeutic challenges. This study delves into the intricate etiology of asthma, emphasizing the pivotal roles of immune cells such as T cells, eosinophils, and mast cells in its pathogenesis. The recent emergence of monoclonal antibodies, including Mepolizumab, Reslizumab, and Benralizumab, offers therapeutic promise, yet their efficacy varies due to the heterogeneous nature of asthma. Recognizing the potential of personalized medicine, this research underscores the need for a comprehensive understanding of asthma’s immunological diversity. We employ ssGSEA and LASSO algorithms to identify differentially expressed immune cells and utilize machine learning techniques, including XGBoost and Random Forest, to predict severe asthma outcomes and identify key genes associated with immune cells. Using a murine asthma model and an online database, we aim to elucidate distinct immune-centric asthma subtypes. This study seeks to provide novel insights into the diagnosis and classification of severe asthma through a transcriptomic lens.
That’s not a Hybrid: How to Distinguish Patterns of Admixture and Isolation-by-Distance
Authors: Wiens Ben J. , Colella Jocelyn P. 
Published: April 2024

AbstractDescribing naturally occurring genetic variation is a fundamental goal of molecular phylogeography and population genetics. Popular methods for this task includeSTRUCTURE, a model-based algorithm that assigns individuals to genetic clusters, and Principal Component Analysis (PCA), a parameter-free method. The ability ofSTRUCTUREto infer mixed ancestry makes it popular for documenting natural hybridization, which is of considerable interest to evolutionary biologists, given that such systems provide a window into the speciation process. YetSTRUCTUREcan produce misleading results when its underlying assumptions are violated, like when genetic variation is distributed continuously. To test the ability ofSTRUCTUREand PCA to accurately distinguish admixture from continuous variation, we use forward-time simulations to generate population genetic data under three demographic scenarios: two involving admixture and one with isolation-by-distance (IBD).STRUCTUREand PCA alone cannot distinguish admixture from IBD, but complementing these analyses with triangle plots, which visualize hybrid index against interclass heterozygosity, provides more accurate inference of demographic history. We demonstrate that triangle plots are robust to missing data, whileSTRUCTUREand PCA are not, and show that setting a low allele frequency difference threshold for AIM identification can accurately characterize the relationship between hybrid index and interclass heterozygosity across demographic histories of admixture and range expansion. WhileSTRUCTUREand PCA provide useful summaries of genetic variation, results should be paired with triangle plots before admixture is inferred.
Pervasive gene flow despite strong and varied reproductive barriers in swordtails
Authors: Aguillon Stepfanie M. , Haase Cox Sophia K. , Langdon Quinn K. , Gunn Theresa R. , Baczenas John J. , Banerjee Shreya M. , Donny Alexandra E. , Moran Benjamin M. , Gutiérrez-Rodríguez Carla , Ríos-Cárdenas Oscar , Morris Molly R. , Powell Daniel L. , Schumer Molly 
Published: April 2024

AbstractOne of the mechanisms that can lead to the formation of new species occurs through the evolution of reproductive barriers. However, recent research has demonstrated that hybridization has been pervasive across the tree of life even in the presence of strong barriers. Swordtail fishes (genusXiphophorus) are an emerging model system for studying the interface between these barriers and hybridization. We document overlapping mechanisms that act as barriers between closely related species,X. birchmanniandX. cortezi, by combining genomic sequencing from natural hybrid populations, artificial crosses, behavioral assays, sperm performance, and developmental studies. We show that strong assortative mating plays a key role in maintaining subpopulations with distinct ancestry in natural hybrid populations. Lab experiments demonstrate that artificial F1crosses experience dysfunction: crosses withX. birchmannifemales were largely inviable and crosses withX. cortezifemales had a heavily skewed sex ratio. Using F2hybrids we identify several genomic regions that strongly impact hybrid viability. Strikingly, two of these regions underlie genetic incompatibilities in hybrids betweenX. birchmanniand its sister speciesX. malinche. Our results demonstrate that ancient hybridization has played a role in the origin of this shared genetic incompatibility. Moreover, ancestry mismatch at these incompatible regions has remarkably similar consequences for phenotypes and hybrid survival inX. corteziÔX. birchmannihybrids as inX. malincheÔX. birchmannihybrids. Our findings identify varied reproductive barriers that shape genetic exchange between naturally hybridizing species and highlight the complex evolutionary outcomes of hybridization.Significance StatementBiologists are fascinated by how the diverse species we see on Earth have arisen and been maintained. One driver of this process is the evolution of reproductive barriers between species. Despite the commonality of these barriers, many species still exchange genes through a process called hybridization. Here, we show that related species can have a striking array of reproductive barriers—from genetic interactions that harm hybrids to mate preferences that reduce hybridization in the first place. However, we also find that genetic exchange between these species is very common, and may itself play an important role in the evolution of reproductive barriers. Together, our work highlights the complex web of interactions that impact the origin and persistence of distinct species.
Transcriptional regulation in the absence of Inositol Trisphosphate Receptor Calcium Signaling
Authors: Young Michael , Booth David M. , Smith David , Tigano Marco , Hajnόczky Gyӧrgy , Joseph Suresh K. 
Published: April 2024

ABSTRACTThe activation of IP3receptor (IP3R) Ca2+channels generates agonist-mediated Ca2+signals that regulate a wide range of biological processes. It is therefore surprising that CRISPR induced loss of all three IP3R isoforms (TKO) in HEK293 and HeLa cell lines yields cells that can survive, grow and divide, albeit more slowly than wild-type cells. In an effort to understand the adaptive mechanisms involved, we have examined the activity of key Ca2+dependent transcription factors (NFAT, CREB, AP-1 and NFκb) and signaling pathways using luciferase-reporter assays, phosphoprotein immunoblots and whole genome transcriptomic studies. In addition the role of protein kinase C (PKC) was investigated with inhibitors and siRNA knockdown. The data showed that agonist-mediated NFAT activation was lost but CREB activation was maintained in IP3R TKO cells. Under base-line conditions transcriptome analysis indicated the differential expression (DEG) of 828 and 311 genes in IP3R TKO HEK293 or HeLa cells, respectively, with only 18 genes being in common. In summary three main adaptations in TKO cells are identified in this study: 1) increased basal activity of NFAT, CREB, AP-1 and NFκb; 2) an increased reliance on Ca2+-insensitive PKC isoforms; and 3) increased production of reactive oxygen species and upregulation of antioxidant defense enzymes. We suggest that whereas wild-type cells rely on a Ca2+and DAG signal to respond to stimuli, the TKO cells utilize the adaptations to allow key signaling pathways (e.g. PKC, Ras/MAPK, CREB) to transition to the activated state using a DAG signal alone.
GWASBrewer: An R Package for Simulating Realistic GWAS Summary Statistics
Authors: Morrison Jean 
Published: April 2024

1AbstractMany statistical genetics analysis methods make use of GWAS summary statistics. Best statistical practice requires evaluating these methods in simulations against a known truth. Ideally, these simulations should be as realistic as possible. However, simulating summary statistics by first simulating individual genotype and phenotype data is extremely computationally demanding, especially when large sample sizes or many traits are required. We presentGWASBrewer, an open source R package for direct simulation of GWAS summary statistics. We show that statistics simulated byGWASBrewerhave the same distribution as statistics generated from individual level data, and can be produced at a fraction of the computational expense. Additionally,GWASBrewercan simulate standard error estimates, something that is typically not done when sampling summary statistics directly.GWASBreweris highly flexible, allowing the user to simulate data for multiple traits connected by causal effects and with complex distributions of effect sizes. We demonstrate example uses ofGWASBrewerfor evaluating Mendelian randomization, polygenic risk score, and heritability estimation methods.
Physical cell-cell interactions regulate transcriptional programmes that control the responses of high grade serous ovarian cancer patients to therapy
Authors: Hameed Sodiq A. , Kolch Walter , Brennan Donal J. , Zhernovkov Vadim 
Published: April 2024

ABSTRACTThe tumour microenvironment is composed of a complex cellular network involving cancer, stromal and immune cells in dynamic interactions. A large proportion of this network relies on direct physical interactions between cells, which may impact patient responses to clinical therapy. Doublets in scRNA-seq are usually excluded from analysis. However, they may represent directly interacting cells. To decipher the physical interaction landscape in relation to clinical prognosis, we inferred a physical cell-cell interaction (PCI) network from ‘biological’ doublets in a scRNA-seq dataset of approximately 18,000 cells, obtained from 7 treatment-naive ovarian cancer patients. Focusing on cancer-stromal PCIs, we uncovered molecular interaction networks and transcriptional landscapes that stratified patients in respect to their clinical responses to standard therapy. Good responders featured PCIs involving immune cells interacting with other cell types including cancer cells. Poor responders lacked immune cell interactions, but showed a high enrichment of cancer-stromal PCIs. To explore the molecular differences between cancer-stromal PCIs between responders and non-responders, we identified correlating gene signatures. We constructed ligand-receptor interaction networks and identified associated downstream pathways. The reconstruction of gene regulatory networks and trajectory analysis revealed distinct transcription factor (TF) clusters and gene modules that separated doublet cells by clinical outcomes. Our results indicate (i) that transcriptional changes resulting from PCIs predict the response of ovarian cancer patients to standard therapy, (ii) that immune reactivity of the host against the tumour enhances the efficacy of therapy, and (iii) that cancer-stromal cell interaction can have a dual effect either supporting or inhibiting therapy responses.
Dense Investigation of Variability in Affect (DIVA): A Neuroimaging Study of Premenopausal Female Participants
Authors: Bottenhorn Katherine L. , Salo Taylor , Peraza Julio A. , Riedel Michael C. , Flannery Jessica S. , Kimbler Adam , Toll Alfredo , Suarez Diego , Cruz Francis M. , Zagales Israel , Caldes Nayade , Dolan Olivia , Zagales Ruth , Sutherland Matthew T. , Laird Robert W. , Laird Angela R. 
Published: April 2024

AbstractThe rise of large neuroimaging datasets and multi-dataset mega-analyses brings the power to study interindividual differences in brain structure and function on a heretofore unseen scale. However, unknown and poorly characterized intra-individual variability continues to undermine the detection of robust brain-behavior associations and, ultimately, our understanding of the brain on the whole. Women’s and reproductive health underlie variability in more than half of the population, but have long been overlooked in the study of both inter- and intra-individual differences in the brain. To this end, the Dense Investigation of Variability in Affect (DIVA) Study was designed to study intra-individual variability in the brain and behavior across the menstrual cycle in a small cohort of premenopausal female participants. The DIVA Study acquired weekly actigraphy, self-report, biospecimen, and both functional and structural magnetic resonance imaging data with concurrent peripheral physiological recordings. These data facilitate the study of several common sources of variability in the brain and behavior: the menstrual cycle and ovarian hormones, sleep, stress, exercise, and exogenous sources of hemodynamic variability.
NovoBoard: a comprehensive framework for evaluating the false discovery rate and accuracy of de novo peptide sequencing
Authors: Tran Ngoc Hieu , Qiao Rui , Mao Zeping , Pan Shengying , Zhang Qing , Li Wenting , Xin Lei , Li Ming , Shan Baozhen 
Published: April 2024

AbstractDe novo peptide sequencing is a fundamental research area in mass spectrometry (MS) based proteomics. However, those methods have often been evaluated using a couple of simple metrics that do not fully reflect their overall performance. Moreover, there has not been an established method to estimate the false discovery rate (FDR) and the significance of de novo peptide-spectrum matches (PSMs). Here we propose NovoBoard, a comprehensive framework to evaluate the performance of de novo peptide sequencing methods. The framework consists of diverse benchmark datasets (including tryptic, nontryptic, immunopeptidomics, and different species), and a standard set of accuracy metrics to evaluate the fragment ions, amino acids, and peptides of the de novo results. More importantly, a new approach is designed to evaluate de novo peptide sequencing methods on target-decoy spectra and to estimate their FDRs. Our results thoroughly reveal the strengths and weaknesses of different de novo peptide sequencing methods, and how their performances depend on specific applications and the types of data. Our FDR estimation also shows that some tools may perform better than the others in distinguishing between de novo PSMs and random matches, and can be used to assess the significance of de novo PSMs.