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.

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.

GENEVIC: GENetic data exploration and visualization via intelli- gent 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://genevic- anath2024.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). SUPPLEMENTARY INFORMATION: Available at Bioinformatics online and at https://github.com/bsml320/GENEVIC_Supplementary.git (also at https://github.com/anath2110/GENEVIC_Supplementary.git).

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.

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.

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.

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.