Scupa: Single-cell unified polarization assessment of immune cells using the single-cell foundation model.

Immune cells undergo cytokine-driven polarization in respond to diverse stimuli. This process significantly modulates their transcriptional profiles and functional states. Although single-cell RNA sequencing (scRNA-seq) has advanced our understanding of immune responses across various diseases or conditions, currently there lacks a method to systematically examine cytokine effects and immune cell polarization. To address this gap, we developed Single-cell unified polarization assessment (Scupa), the first computational method for comprehensive immune cell polarization analysis. Scupa is trained on data from the Immune Dictionary, which characterizes 66 cytokine-driven polarization states across 14 immune cell types. By leveraging the cell embeddings from the Universal Cell Embeddings model, Scupa effectively identifies polarized cells in new datasets generated from different species and experimental conditions. Applications of Scupa in independent datasets demonstrated its accuracy in classifying polarized cells and further revealed distinct polarization profiles in tumor-infiltrating myeloid cells across cancers. Scupa complements conventional single-cell data analysis by providing new insights into immune cell polarization, and it holds promise for assessing molecular effects or identifying therapeutic targets in cytokine-based therapies.

Loss of p53 and SMAD4 induces adenosquamous subtype pancreatic cancer in the absence of an oncogenic KRAS mutation.

Pancreatic cancer is associated with an oncogenic KRAS mutation in approximately 90% of cases. However, a non-negligible proportion of pancreatic cancer cases harbor wild-type KRAS (KRAS-WT). This study establishes genetically engineered mouse models that develop spontaneous pancreatic cancer in the context of KRAS-WT. The Trp53loxP/loxP;Smad4loxP/loxP;Pdx1-Cre (PPSSC) mouse model harbors KRAS-WT and loss of Trp53/Smad4. The Trp53loxP/loxP;Tgfbr2loxP/loxP;Pdx1-Cre (PPTTC) mouse model harbors KRAS-WT and loss of Trp53/Tgfbr2. We identify that either Trp53/Smad4 loss or Trp53/Tgfbr2 loss can induce spontaneous pancreatic tumor formation in the absence of an oncogenic KRAS mutation. The Trp53/Smad4 loss and Trp53/Tgfbr2 loss mouse models exhibit distinct pancreatic tumor histological features, as compared to oncogenic KRAS-driven mouse models. Furthermore, KRAS-WT pancreatic tumors with Trp53/Smad4 loss reveal unique histological features of pancreatic adenosquamous carcinoma (PASC). Single-cell RNA sequencing (scRNA-seq) analysis reveals the distinct tumor immune microenvironment landscape of KRAS-WT (PPSSC) pancreatic tumors as compared with that of oncogenic KRAS-driven pancreatic tumors.

Astrocyte-induced Cdk5 expedites breast cancer brain metastasis by suppressing MHC-I expression to evade immune recognition.

Brain metastases (BrMs) evade the immune response to develop in the brain, yet the mechanisms of BrM immune evasion remains unclear. This study shows that brain astrocytes induce the overexpression of neuronal-specific cyclin-dependent kinase 5 (Cdk5) in breast cancer-derived BrMs, which facilitates BrM outgrowth in mice. Cdk5-overexpressing BrMs exhibit reduced expression and function of the class I major histocompatibility complex (MHC-I) and antigen-presentation pathway, which are restored by inhibiting Cdk5 genetically or pharmacologically, as evidenced by single-cell RNA sequencing and functional studies. Mechanistically, Cdk5 suppresses MHC-I expression on the cancer cell membrane through the Irf2bp1-Stat1-importin α-Nlrc5 pathway, enabling BrMs to avoid recognition by T cells. Treatment with roscovitine-a clinically applicable Cdk5 inhibitor-alone or combined with immune checkpoint inhibitors, significantly reduces BrM burden and increases tumour-infiltrating functional CD8+ lymphocytes in mice. Thus, astrocyte-induced Cdk5 overexpression endorses BrM immune evasion, whereas therapeutically targeting Cdk5 markedly improves the efficacy of immune checkpoint inhibitors and inhibits BrM growth.

Microglial type I interferon signaling mediates chronic stress-induced synapse loss and social behavior deficits.

Inflammation and synapse loss have been associated with deficits in social behavior and are involved in pathophysiology of many neuropsychiatric disorders. Synapse loss, characterized by reduction in dendritic spines can significantly disrupt synaptic connectivity and neural circuitry underlying social behavior. Chronic stress is known to induce loss of spines and dendrites in the prefrontal cortex (PFC), a brain region implicated in social behavior. However, the underlying mechanisms are not well understood. In the present study, we investigated the role of type I Interferon (IFN-I) signaling in chronic unpredictable stress (CUS)-induced synapse loss and behavior deficits in mice. We found increased expression of type I IFN receptor (IFNAR) in microglia following CUS. Conditional knockout of microglial IFNAR in adult mice rescued CUS-induced social behavior deficits and synapse loss. Bulk RNA sequencing data show that microglial IFNAR deletion attenuated CUS-mediated changes in the expression of genes such as Keratin 20 (Krt20), Claudin-5 (Cldn5) and Nuclear Receptor Subfamily 4 Group A Member 1 (Nr4a1) in the PFC. Cldn5 and Nr4a1 are known for their roles in synaptic plasticity. Krt20 is an intermediate filament protein responsible for the structural integrity of epithelial cells. The reduction in Krt20 following CUS presents a novel insight into the potential contribution of cytokeratin in stress-induced alterations in neuroplasticity. Overall, these results suggest that microglial IFNAR plays a critical role in regulating synaptic plasticity and social behavior deficits associated with chronic stress conditions.

Single-nucleus multiomics reveals the disrupted regulatory programs in three brain regions of sporadic early-onset Alzheimer's disease.

Sporadic early-onset Alzheimer's disease (sEOAD) represents a significant but less-studied subtype of Alzheimer's disease (AD). Here, we generated a single-nucleus multiome atlas derived from the postmortem prefrontal cortex, entorhinal cortex, and hippocampus of nine individuals with or without sEOAD. Comprehensive analyses were conducted to delineate cell type-specific transcriptomic changes and linked candidate cis-regulatory elements (cCREs) across brain regions. We prioritized seven conservative transcription factors in glial cells in multiple brain regions, including RFX4 in astrocytes and IKZF1 in microglia, which are implicated in regulating sEOAD-associated genes. Moreover, we identified the top 25 altered intercellular signaling between glial cells and neurons, highlighting their regulatory potential on gene expression in receiver cells. We reported 38 cCREs linked to sEOAD-associated genes overlapped with late-onset AD risk loci, and sEOAD cCREs enriched in neuropsychiatric disorder risk loci. This atlas helps dissect transcriptional and chromatin dynamics in sEOAD, providing a key resource for AD research.

Preventive Treatment with a CD73 Small Molecule Inhibitor Enhances Immune Surveillance in K-Ras Mutant Pancreatic Intraepithelial Neoplasia.

Immunoprevention is an emerging consideration for solid tumors, including pancreatic ductal adenocarcinoma (PDAC). We and others have shown that Kras mutations in genetic models of spontaneous pancreatic intraepithelial neoplasia (PanIN), which is a precursor to PDAC, results in CD73 expression in the neoplastic epithelium and some populations of infiltrating immune cells, including macrophages and CD8 T cells. CD73 is an ecto-enzyme that converts extracellular adenosine monophosphate to adenosine, a critical immune inhibitory molecule in PDAC. We hypothesized inhibition of CD73 would reduce the incidence of PanIN formation and alter the immune microenvironment. To test our hypothesis, we used the KrasG12D; PdxCre1 (KC) genetically engineered mouse model and tested the utility of AB-680, a small molecule inhibitor targeting CD73, to inhibit PanIN progression. AB-680, or vehicle control, was administered using oral gavage delivery 3 days/week at 10 mg/kg, beginning when the mice were 2 months old and lasting 3 months. We euthanized the mice at 5 months old. In the KC model, we quantified significantly less pancreatitis, early and advanced PanIN, and quantified a significant increase in M1 macrophages in AB-680-treated mice. Single-cell RNA sequencing (scRNA-seq) of pancreata of AB-680-treated mice revealed increased infiltration of CD4+ T cells, CD8+ T cells, and mature B cells. The scRNA-seq analysis showed that CD73 inhibition reduced M2 macrophages, acinar, and PanIN cell populations. CD73 inhibition enhanced immune surveillance and expanded unique clonotypes of TCR and BCR, indicating that inhibition of CD73 augments adaptive immunity early in the neoplastic microenvironment. Prevention Relevance: Previous studies found PanIN lesions in healthy pancreata. Not all progress to PDAC, suggesting a window for enhanced antitumor immunity through immunoprevention therapy. CD73 inhibition in our study prevents PanIN progression, reduces immune-suppressive macrophages and expands TCR and BCR unique clonotypes, highlighting an encouraging therapeutic avenue for high-risk individuals.

GENEVIC: GENetic data Exploration and Visualization via Intelligent interactive Console.

SUMMARY: The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge. Introducing GENEVIC, an AI-driven chat framework that tackles this challenge by bridging the gap between genetic data generation and biomedical knowledge discovery. Leveraging generative AI, notably ChatGPT, it serves as a biologist's "copilot." It automates the analysis, retrieval, and visualization of customized domain-specific genetic information, and integrates functionalities to generate protein interaction networks, enrich gene sets, and search scientific literature from PubMed, Google Scholar, and arXiv, making it a comprehensive tool for biomedical research. In its pilot phase, GENEVIC is assessed using a curated database that ranks genetic variants associated with Alzheimer's disease, schizophrenia, and cognition, based on their effect weights from the Polygenic Score (PGS) Catalog, thus enabling researchers to prioritize genetic variants in complex diseases. GENEVIC's operation is user-friendly, accessible without any specialized training, secured by Azure OpenAI's HIPAA-compliant infrastructure, and evaluated for its efficacy through real-time query testing. As a prototype, GENEVIC is set to advance genetic research, enabling informed biomedical decisions. AVAILABILITY AND IMPLEMENTATION: GENEVIC is publicly accessible at https://genevicanath2024.streamlit.app. The underlying code is open-source and available via GitHub at https://github.com/bsml320/GENEVIC.git (also at https://github.com/anath2110/GENEVIC.git).

Single-nucleus multiomics reveals the disrupted regulatory programs in three brain regions of sporadic early-onset Alzheimer's disease.

Sporadic early-onset Alzheimer's disease (sEOAD) represents a significant but less-studied subtype of Alzheimer's disease (AD). Here, we generated a single-nucleus multiome atlas derived from the postmortem prefrontal cortex, entorhinal cortex, and hippocampus of nine individuals with or without sEOAD. Comprehensive analyses were conducted to delineate cell type-specific transcriptomic changes and linked candidate cis- regulatory elements (cCREs) across brain regions. We prioritized seven conservative transcription factors in glial cells in multiple brain regions, including RFX4 in astrocytes and IKZF1 in microglia, which are implicated in regulating sEOAD-associated genes. Moreover, we identified the top 25 altered intercellular signaling between glial cells and neurons, highlighting their regulatory potential on gene expression in receiver cells. We reported 38 cCREs linked to sEOAD-associated genes overlapped with late-onset AD risk loci, and sEOAD cCREs enriched in neuropsychiatric disorder risk loci. This atlas helps dissect transcriptional and chromatin dynamics in sEOAD, providing a key resource for AD research.

An improved method for diagnosis of Parkinson's disease using deep learning models enhanced with metaheuristic algorithm.

Parkinson's disease (PD) is challenging for clinicians to accurately diagnose in the early stages. Quantitative measures of brain health can be obtained safely and non-invasively using medical imaging techniques like magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT). For accurate diagnosis of PD, powerful machine learning and deep learning models as well as the effectiveness of medical imaging tools for assessing neurological health are required. This study proposes four deep learning models with a hybrid model for the early detection of PD. For the simulation study, two standard datasets are chosen. Further to improve the performance of the models, grey wolf optimization (GWO) is used to automatically fine-tune the hyperparameters of the models. The GWO-VGG16, GWO-DenseNet, GWO-DenseNet + LSTM, GWO-InceptionV3 and GWO-VGG16 + InceptionV3 are applied to the T1,T2-weighted and SPECT DaTscan datasets. All the models performed well and obtained near or above 99% accuracy. The highest accuracy of 99.94% and AUC of 99.99% is achieved by the hybrid model (GWO-VGG16 + InceptionV3) for T1,T2-weighted dataset and 100% accuracy and 99.92% AUC is recorded for GWO-VGG16 + InceptionV3 models using SPECT DaTscan dataset.

Targeting IGF2 to reprogram the tumor microenvironment for enhanced viro-immunotherapy.

BACKGROUND: The FDA approval of oncolytic herpes simplex-1 virus (oHSV) therapy underscores its therapeutic promise and safety as a cancer immunotherapy. Despite this promise, the current efficacy of oHSV is significantly limited to a small subset of patients largely due to the resistance in tumor and tumor microenvironment (TME). METHODS: RNA sequencing (RNA-Seq) was used to identify molecular targets of oHSV resistance. Intracranial human and murine glioma or breast cancer brain metastasis (BCBM) tumor-bearing mouse models were employed to elucidate the mechanism underlying oHSV therapy-induced resistance. RESULTS: Transcriptome analysis identified IGF2 as one of the top-secreted proteins following oHSV treatment. Moreover, IGF2 expression was significantly upregulated in 10 out of 14 recurrent GBM patients after treatment with oHSV, rQNestin34.5v.2 (71.4%; P = .0020) (ClinicalTrials.gov, NCT03152318). Depletion of IGF2 substantially enhanced oHSV-mediated tumor cell killing in vitro and improved survival of mice bearing BCBM tumors in vivo. To mitigate the oHSV-induced IGF2 in the TME, we constructed a novel oHSV, oHSV-D11mt, secreting a modified IGF2R domain 11 (IGF2RD11mt) that acts as IGF2 decoy receptor. Selective blocking of IGF2 by IGF2RD11mt significantly increased cytotoxicity, reduced oHSV-induced neutrophils/PMN-MDSCs infiltration, and reduced secretion of immune suppressive/proangiogenic cytokines, while increased CD8 + cytotoxic T lymphocytes (CTLs) infiltration, leading to enhanced survival in GBM or BCBM tumor-bearing mice. CONCLUSIONS: This is the first study reporting that oHSV-induced secreted IGF2 exerts a critical role in resistance to oHSV therapy, which can be overcome by oHSV-D11mt as a promising therapeutic advance for enhanced viro-immunotherapy.

Aging- and alcohol-associated spatial transcriptomic signature in mouse acute pancreatitis reveals heterogeneity of inflammation and potential pathogenic factors.

The rapidly aging population is consuming more alcohol, leading to increased alcohol-associated acute pancreatitis (AAP) with high mortality. However, the mechanisms remain undefined, and currently there are no effective therapies available. This study aims to elucidate aging- and alcohol-associated spatial transcriptomic signature by establishing an aging AAP mouse model and applying Visium spatial transcriptomics for understanding of the mechanisms in the context of the pancreatic tissue. Upon alcohol diet feeding and caerulein treatment, aging mice (18 months) developed significantly more severe AAP with 5.0-fold increase of injury score and 2.4-fold increase of amylase compared to young mice (3 months). Via Visium spatial transcriptomics, eight distinct tissue clusters were revealed from aggregated transcriptomes of aging and young AAP mice: five acinar, two stromal, and one islet, which were then merged into three clusters: acinar, stromal, and islet for the comparative analysis. Compared to young AAP mice, > 1300 differentially expressed genes (DEGs) and approximately 3000 differentially regulated pathways were identified in aging AAP mice. The top five DEGs upregulated in aging AAP mice include Mmp8, Ppbp, Serpina3m, Cxcl13, and Hamp with heterogeneous distributions among the clusters. Taken together, this study demonstrates spatial heterogeneity of inflammatory processes in aging AAP mice, offering novel insights into the mechanisms and potential drivers for AAP development. KEY MESSAGES: Mechanisms regarding high mortality of AAP in aging remain undefined. An aging AAP mouse model was developed recapturing clinical exhibition in humans. Spatial transcriptomics identified contrasted DEGs in aging vs. young AAP mice. Top five DEGs were Mmp8, Ppbp, Serpina3m, Cxcl13, and Hamp in aging vs. young AAP mice. Our findings shed insights for identification of molecular drivers in aging AAP.

Improved liver disease prediction from clinical data through an evaluation of ensemble learning approaches.

PURPOSE: Liver disease causes two million deaths annually, accounting for 4% of all deaths globally. Prediction or early detection of the disease via machine learning algorithms on large clinical data have become promising and potentially powerful, but such methods often have some limitations due to the complexity of the data. In this regard, ensemble learning has shown promising results. There is an urgent need to evaluate different algorithms and then suggest a robust ensemble algorithm in liver disease prediction. METHOD: Three ensemble approaches with nine algorithms are evaluated on a large dataset of liver patients comprising 30,691 samples with 11 features. Various preprocessing procedures are utilized to feed the proposed model with better quality data, in addition to the appropriate tuning of hyperparameters and selection of features. RESULTS: The models' performances with each algorithm are extensively evaluated with several positive and negative performance metrics along with runtime. Gradient boosting is found to have the overall best performance with 98.80% accuracy and 98.50% precision, recall and F1-score for each. CONCLUSIONS: The proposed model with gradient boosting bettered in most metrics compared with several recent similar works, suggesting its efficacy in predicting liver disease. It can be further applied to predict other diseases with the commonality of predicate indicators.

Sex differences in metabolic adaptation in infants with cyanotic congenital heart disease.

BACKGROUND: Female infants with congenital heart disease (CHD) face significantly higher postoperative mortality rates after adjusting for cardiac complexity. Sex differences in metabolic adaptation to cardiac stressors may be an early contributor to cardiac dysfunction. In adult diseases, hypoxic/ischemic cardiomyocytes undergo a cardioprotective metabolic shift from oxidative phosphorylation to glycolysis which appears to be regulated in a sexually dimorphic manner. We hypothesize sex differences in cardiac metabolism are present in cyanotic CHD and detectable as early as the infant period. METHODS: RNA sequencing was performed on blood samples (cyanotic CHD cases, n = 11; controls, n = 11) and analyzed using gene set enrichment analysis (GSEA). Global plasma metabolite profiling (UPLC-MS/MS) was performed using a larger representative cohort (cyanotic CHD, n = 27; non-cyanotic CHD, n = 11; unaffected controls, n = 12). RESULTS: Hallmark gene sets in glycolysis, fatty acid metabolism, and oxidative phosphorylation were significantly enriched in cyanotic CHD females compared to male counterparts, which was consistent with metabolomic differences between sexes. Minimal sex differences in metabolic pathways were observed in normoxic patients (both controls and non-cyanotic CHD cases). CONCLUSION: These observations suggest underlying differences in metabolic adaptation to chronic hypoxia between males and females with cyanotic CHD. IMPACT: Children with cyanotic CHD exhibit sex differences in utilization of glycolysis vs. fatty acid oxidation pathways to meet the high-energy demands of the heart in the neonatal period. Transcriptomic and metabolomic results suggest that under hypoxic conditions, males and females undergo metabolic shifts that are sexually dimorphic. These sex differences were not observed in neonates in normoxic conditions (i.e., non-cyanotic CHD and unaffected controls). The involved metabolic pathways are similar to those observed in advanced heart failure, suggesting metabolic adaptations beginning in the neonatal period may contribute to sex differences in infant survival.

Cell-specific regulation of the circadian clock by BMAL1 in the paraventricular nucleus: Implications for regulation of systemic biological rhythms.

Circadian rhythms are internal biological rhythms driving temporal tissue-specific, metabolic programs. Loss of the circadian transcription factor BMAL1 in the paraventricular nucleus (PVN) of the hypothalamus reveals its importance in metabolic rhythms, but its functions in individual PVN cells are poorly understood. Here, loss of BMAL1 in the PVN results in arrhythmicity of processes controlling energy balance and alters peripheral diurnal gene expression. BMAL1 chromatin immunoprecipitation sequencing (ChIP-seq) and single-nucleus RNA sequencing (snRNA-seq) reveal its temporal regulation of target genes, including oxytocin (OXT), and restoring circulating OXT peaks in BMAL1-PVN knockout (KO) mice rescues absent activity rhythms. While glutamatergic neurons undergo day/night changes in expression of genes involved in cell morphogenesis, astrocytes and oligodendrocytes show gene expression changes in cytoskeletal organization and oxidative phosphorylation. Collectively, our findings show diurnal gene regulation in neuronal and non-neuronal PVN cells and that BMAL1 contributes to diurnal OXT secretion, which is important for systemic diurnal rhythms.

Systematic transcriptome profiling of hPSC-derived osteoblasts unveils CORIN's mastery in governing osteogenesis through CEBPD modulation.

The commitment of stem cells to differentiate into osteoblasts is a highly regulated and complex process that involves the coordination of extrinsic signals and intrinsic transcriptional machinery. While rodent osteoblastic differentiation has been extensively studied, research on human osteogenesis has been limited by cell sources and existing models. Here, we systematically dissect human pluripotent stem cell-derived osteoblasts to identify functional membrane proteins and their downstream transcriptional networks involved in human osteogenesis. Our results reveal an enrichment of type II transmembrane serine protease CORIN in humans but not rodent osteoblasts. Functional analyses demonstrated that CORIN depletion significantly impairs osteogenesis. Genome-wide chromatin immunoprecipitation enrichment and mechanistic studies show that p38 MAPK-mediated CCAAT enhancer binding protein delta (CEBPD) upregulation is required for CORIN-modulated osteogenesis. Contrastingly, the type I transmembrane heparan sulfate proteoglycan SDC1 enriched in mesenchymal stem cells exerts a negative regulatory effect on osteogenesis through a similar mechanism. Chromatin immunoprecipitation-seq, bulk and single-cell transcriptomes, and functional validations indicated that CEBPD plays a critical role in controlling osteogenesis. In summary, our findings uncover previously unrecognized CORIN-mediated CEBPD transcriptomic networks in driving human osteoblast lineage commitment.

DeepFace: Deep-learning-based framework to contextualize orofacial-cleft-related variants during human embryonic craniofacial development.

Orofacial clefts (OFCs) are among the most common human congenital birth defects. Previous multiethnic studies have identified dozens of associated loci for both cleft lip with or without cleft palate (CL/P) and cleft palate alone (CP). Although several nearby genes have been highlighted, the "casual" variants are largely unknown. Here, we developed DeepFace, a convolutional neural network model, to assess the functional impact of variants by SNP activity difference (SAD) scores. The DeepFace model is trained with 204 epigenomic assays from crucial human embryonic craniofacial developmental stages of post-conception week (pcw) 4 to pcw 10. The Pearson correlation coefficient between the predicted and actual values for 12 epigenetic features achieved a median range of 0.50-0.83. Specifically, our model revealed that SNPs significantly associated with OFCs tended to exhibit higher SAD scores across various variant categories compared to less related groups, indicating a context-specific impact of OFC-related SNPs. Notably, we identified six SNPs with a significant linear relationship to SAD scores throughout developmental progression, suggesting that these SNPs could play a temporal regulatory role. Furthermore, our cell-type specificity analysis pinpointed the trophoblast cell as having the highest enrichment of risk signals associated with OFCs. Overall, DeepFace can harness distal regulatory signals from extensive epigenomic assays, offering new perspectives for prioritizing OFC variants using contextualized functional genomic features. We expect DeepFace to be instrumental in accessing and predicting the regulatory roles of variants associated with OFCs, and the model can be extended to study other complex diseases or traits.

VER-Net: a hybrid transfer learning model for lung cancer detection using CT scan images.

BACKGROUND: Lung cancer is the second most common cancer worldwide, with over two million new cases per year. Early identification would allow healthcare practitioners to handle it more effectively. The advancement of computer-aided detection systems significantly impacted clinical analysis and decision-making on human disease. Towards this, machine learning and deep learning techniques are successfully being applied. Due to several advantages, transfer learning has become popular for disease detection based on image data. METHODS: In this work, we build a novel transfer learning model (VER-Net) by stacking three different transfer learning models to detect lung cancer using lung CT scan images. The model is trained to map the CT scan images with four lung cancer classes. Various measures, such as image preprocessing, data augmentation, and hyperparameter tuning, are taken to improve the efficacy of VER-Net. All the models are trained and evaluated using multiclass classifications chest CT images. RESULTS: The experimental results confirm that VER-Net outperformed the other eight transfer learning models compared with. VER-Net scored 91%, 92%, 91%, and 91.3% when tested for accuracy, precision, recall, and F1-score, respectively. Compared to the state-of-the-art, VER-Net has better accuracy. CONCLUSION: VER-Net is not only effectively used for lung cancer detection but may also be useful for other diseases for which CT scan images are available.