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Type I IFN drives unconventional IL-1β secretion in lupus monocytes.
No full textOpsonization of red blood cells that retain mitochondria (Mito + RBCs), a feature of systemic lupus erythema- tosus (SLE), triggers type I interferon (IFN) production in macrophages. We report that monocytes (Mos) co- produce IFN and mature interleukin-1b (mIL-1b) upon Mito + RBC opsonization. IFN expression depended on cyclic GMP-AMP synthase (cGAS) and RIG-I-like receptors’ (RLRs) sensing of Mito + RBC-derived mitochon- drial DNA (mtDNA) and mtRNA, respectively. Interleukin-1b (IL-1b) production was initiated by the RLR anti- viral signaling adaptor (MAVS) pathway recognition of Mito + RBC-derived mtRNA. This led to the cytosolic release of Mo mtDNA, which activated the inflammasome. Importantly, mIL-1b secretion was independent of gasdermin D (GSDMD) and pyroptosis but relied on IFN-inducible myxovirus-resistant protein 1 (MxA), which facilitated the incorporation of mIL-1b into a trans-Golgi network (TGN)-mediated secretory pathway. RBC internalization identified a subset of blood Mo expressing IFN-stimulated genes (ISGs) that released mIL-1b and expanded in SLE patients with active disease
Assessing Immune Subtype Diversity in Mice
No full textImmunotherapies are promising cancer treatments that leverage the body’s existing immune system to eliminate cancers. The existing tumor immune microenvironment, critical to immunotherapy response, remains poorly understood due to its complexity. A seminal study by Thorsson et. al, delineated six immune subtypes across 10,000 human tumors spanning 33 cancer types in The Cancer Genome Atlas. These immune subtypes were defined using transcriptomics and characterized by different immune cell proportions, tumor-immune signaling pathways, and patient survival. Such categorization aims to effectively categorize tumor immune states to promote more specific immunology research informed by the common characteristics of many tumors. Our study ascertained if Thorsson’s immune subtypes concept could be extended to mice tumors to create models that reflect the tumor immune state of many human tumors—using a dataset of RNA-sequencing of 2,842 mouse tumors curated from publicly available data, an existing machine-learning tool was employed to predict the immune subtypes defined by Thorsson\u27s study. All six immune subtypes were observed with largely conserved gene signature scores and immune cell type proportions across species. Tumor genetics, tissue of origin, and cancer type emerged as primary determinants for immune subtype classification in mice. The study provides a first step in understanding the heterogeneity of tumor immune microenvironments shared by mouse and human tumors and points to the factors that might be modulated to direct the immune microenvironment toward a desired configuration in mouse models of human cancer
Characterizing Lung Cancer Metastasis in the Novel KPDT Mouse Model
No full textNon-small cell lung cancer, lung adenocarcinoma (LUAD) in particular, is the most prevalent form of lung cancer and possesses the highest rate of lethality of all oncogenic diseases. Metastatic progression is a defining feature of later stage cancer progression and is the primary cause for cancer deaths. Despite the overwhelming presence and lethality of LUAD in humans, established mouse models do not reliably develop metastatic disease. This project aimed to characterize metastatic potential in the novel KPDT mouse model through histological and flow cytometric methods. Additionally, a transcriptomic analysis was conducted from bulk RNA sequencing data to identify differentially expressed genes in two KPDT models with or without Dicer1 inhibition. The results confirmed the reliability of the mT/mG double fluorescent Cre reporter to differentiate healthy and cancerous tissue in tumor-bearing mice. Histological and flow cytometry experiments produced refined laboratory procedures to be used in yielding reproducible results for future analyses. The Transcriptomic analysis revealed many significantly differentiated phenotypes capable of being the subjects of therapeutics
Classroom to career: Implementation considerations for engaging students with meaningful DNA sequencing learning opportunities.
No full textWhile DNA science is fascinating in its own right, successfully executing a DNA sequencing exercise with high school or undergraduate students is an extremely valuable learning experience. DNA sequencing is common as a laboratory exercise embedded within undergraduate courses. At the high school level, DNA sequencing experiments are equally engaging for students but normally require external programmatic support or connections to a university core facility. To achieve age-appropriate learning goals students need to be actively engaged in the laboratory or sequence analysis activity. When students take ownership of a sequencing project and the downstream data, they gain self-confidence and make a wide variety of content-based learning gains. Highly successful integration of DNA sequencing into high school and undergraduate classrooms requires achievable laboratory and data analysis workflows, user-friendly web resources/platforms for data analysis and dedicated faculty or a network of professionals that can support larger-scale, crowd-sourced sequencing efforts. This chapter discusses best-practices for integrating DNA sequencing into high school and undergraduate classes and highlights a few examples of successful, class-based research integrations
Discovery and validation of genes driving drug-intake and related behavioral traits in mice.
Get PDFSubstance use disorders are heritable disorders characterized by compulsive drug use, the biological mechanisms for which remain largely unknown. Genetic correlations reveal that predisposing drug-naïve phenotypes, including anxiety, depression, novelty preference and sensation seeking, are predictive of drug-use phenotypes, thereby implicating shared genetic mechanisms. High-throughput behavioral screening in knockout (KO) mice allows efficient discovery of the function of genes. We used this strategy in two rounds of candidate prioritization in which we identified 33 drug-use candidate genes based upon predisposing drug-naïve phenotypes and ultimately validated the perturbation of 22 genes as causal drivers of substance intake. We selected 19/221 KO strains (8.5%) that had a difference from control on at least one drug-naïve predictive behavioral phenotype and determined that 15/19 (~80%) affected the consumption or preference for alcohol, methamphetamine or both. No mutant exhibited a difference in nicotine consumption or preference which was possibly confounded with saccharin. In the second round of prioritization, we employed a multivariate approach to identify outliers and performed validation using methamphetamine two-bottle choice and ethanol drinking-in-the-dark protocols. We identified 15/401 KO strains (3.7%, which included one gene from the first cohort) that differed most from controls for the predisposing phenotypes. 8 of 15 gene deletions (53%) affected intake or preference for alcohol, methamphetamine or both. Using multivariate and bioinformatic analyses, we observed multiple relations between predisposing behaviors and drug intake, revealing many distinct biobehavioral processes underlying these relationships. The set of mouse models identified in this study can be used to characterize these addiction-related processes further
Three linked variants have opposing regulatory effects on isovaleryl-CoA dehydrogenase gene expression.
No full textWhile genome-wide association studies (GWAS) and positive selection scans identify genomic loci driving human phenotypic diversity, functional validation is required to discover the variant(s) responsible. We dissected the IVD gene locus-which encodes the isovaleryl-CoA dehydrogenase enzyme-implicated by selection statistics, multiple GWAS, and clinical genetics as important to function and fitness. We combined luciferase assays, CRISPR/Cas9 genome-editing, massively parallel reporter assays (MPRA), and a deletion tiling MPRA strategy across regulatory loci. We identified three regulatory variants, including an indel, that may underpin GWAS signals for pulmonary fibrosis and testosterone, and that are linked on a positively selected haplotype in the Japanese population. These regulatory variants exhibit synergistic and opposing effects on IVD expression experimentally. Alleles at these variants lie on a haplotype tagged by the variant most strongly associated with IVD expression and metabolites, but with no functional evidence itself. This work demonstrates how comprehensive functional investigation and multiple technologies are needed to discover the true genetic drivers of phenotypic diversity
Large structural variants in KOLF2.1J are unlikely to compromise neurological disease modeling.
No full textStructured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES): a method for populating knowledge bases using zero-shot learning.
Get PDFMOTIVATION: Creating knowledge bases and ontologies is a time consuming task that relies on manual curation. AI/NLP approaches can assist expert curators in populating these knowledge bases, but current approaches rely on extensive training data, and are not able to populate arbitrarily complex nested knowledge schemas.
RESULTS: Here we present Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES), a Knowledge Extraction approach that relies on the ability of Large Language Models (LLMs) to perform zero-shot learning and general-purpose query answering from flexible prompts and return information conforming to a specified schema. Given a detailed, user-defined knowledge schema and an input text, SPIRES recursively performs prompt interrogation against an LLM to obtain a set of responses matching the provided schema. SPIRES uses existing ontologies and vocabularies to provide identifiers for matched elements. We present examples of applying SPIRES in different domains, including extraction of food recipes, multi-species cellular signaling pathways, disease treatments, multi-step drug mechanisms, and chemical to disease relationships. Current SPIRES accuracy is comparable to the mid-range of existing Relation Extraction methods, but greatly surpasses an LLM\u27s native capability of grounding entities with unique identifiers. SPIRES has the advantage of easy customization, flexibility, and, crucially, the ability to perform new tasks in the absence of any new training data. This method supports a general strategy of leveraging the language interpreting capabilities of LLMs to assemble knowledge bases, assisting manual knowledge curation and acquisition while supporting validation with publicly-available databases and ontologies external to the LLM.
AVAILABILITY AND IMPLEMENTATION: SPIRES is available as part of the open source OntoGPT package: https://github.com/monarch-initiative/ontogpt
A data browsing application for accessing gene and module-level blood transcriptome profiles of healthy pregnant women from high- and low-resource settings.
Get PDFTranscriptome profiling data, generated via RNA sequencing, are commonly deposited in public repositories. However, these data may not be easily accessible or usable by many researchers. To enhance data reuse, we present well-annotated, partially analyzed data via a user-friendly web application. This project involved transcriptome profiling of blood samples from 15 healthy pregnant women in a low-resource setting, taken at 6 consecutive time points beginning from the first trimester. Additional blood transcriptome profiles were retrieved from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) public repository, representing a cohort of healthy pregnant women from a high-resource setting. We analyzed these datasets using the fixed BloodGen3 module repertoire. We deployed a web application, accessible at https://thejacksonlaboratory.shinyapps.io/BloodGen3_Pregnancy/which displays the module-level analysis results from both original and public pregnancy blood transcriptome datasets. Users can create custom fingerprint grid and heatmap representations via various navigation options, useful for reports and manuscript preparation. The web application serves as a standalone resource for exploring blood transcript abundance changes during pregnancy. Alternatively, users can integrate it with similar applications developed for earlier publications to analyze transcript abundance changes of a given BloodGen3 signature across a range of disease cohorts. Database URL: https://thejacksonlaboratory.shinyapps.io/BloodGen3_Pregnancy/