Subtopic Deep Dive
Immunometabolism in Trained Immunity
Research Guide
What is Immunometabolism in Trained Immunity?
Immunometabolism in trained immunity examines how metabolic reprogramming, such as shifts to glycolysis and glutaminolysis, sustains long-term innate immune memory in monocytes and macrophages.
Trained immunity involves epigenetic and metabolic changes that enhance innate responses to secondary stimuli. Key studies link aerobic glycolysis in alveolar macrophages to control of Mycobacterium tuberculosis replication (Gleeson et al., 2016, 305 citations). Over 10 papers from the list explore monocyte phenotypes and mitochondrial metabolism in this context.
Why It Matters
Metabolic modulation of trained immunity provides therapeutic targets for chronic infections like tuberculosis, where M. tuberculosis induces glycolysis essential for bacterial control (Gleeson et al., 2016). In atherosclerosis, monocyte immunometabolism drives plaque inflammation, offering intervention points (Groh et al., 2017). Netea et al. (2020, 2299 citations) highlight its role in health and disease, including autoimmunity and enhanced vaccine responses.
Key Research Challenges
Linking metabolism to epigenetics
Connecting specific metabolic pathways like glycolysis to epigenetic marks in trained immunity remains incomplete. Van der Heijden et al. (2017) discuss epigenetics but note gaps in causal links. This hinders targeted therapies.
Heterogeneity in monocyte responses
Monocyte subsets show variable metabolic shifts during training, complicating predictions. Kapellos et al. (2019, 848 citations) define classical, non-classical, and intermediate phenotypes with distinct behaviors. Standardizing models is needed.
Translating to human diseases
Mouse models inadequately capture human immunometabolic training in pathologies like atherosclerosis. Groh et al. (2017) and Ochando et al. (2022, 347 citations) call for clinical validation. Pharmacological modulation faces safety hurdles.
Essential Papers
Defining trained immunity and its role in health and disease
Mihai G. Netea, Jorge Domínguez‐Andrés, Luis B. Barreiro et al. · 2020 · Nature reviews. Immunology · 2.3K citations
Human Monocyte Subsets and Phenotypes in Major Chronic Inflammatory Diseases
Theodore S. Kapellos, Lorenzo Bonaguro, Ioanna D. Gemünd et al. · 2019 · Frontiers in Immunology · 848 citations
Human monocytes are divided in three major populations; classical (CD14<sup>+</sup>CD16<sup>-</sup>), non-classical (CD14<sup>dim</sup>CD16<sup>+</sup>), and intermediate (CD14<sup>+</sup>CD16<sup>...
Immune evasion and provocation by Mycobacterium tuberculosis
Pallavi Chandra, Steven J. Grigsby, Jennifer A. Philips · 2022 · Nature Reviews Microbiology · 509 citations
Immunology of Aging: the Birth of Inflammaging
Tamàs Fülöp, Anis Larbi, Graham Pawelec et al. · 2021 · Clinical Reviews in Allergy & Immunology · 371 citations
Trained immunity — basic concepts and contributions to immunopathology
Jordi Ochando, Willem J. M. Mulder, Joren C. Madsen et al. · 2022 · Nature Reviews Nephrology · 347 citations
Neutrophil phenotypes and functions in cancer: A consensus statement
Daniela F. Quail, Borko Amulic, Monowar Aziz et al. · 2022 · The Journal of Experimental Medicine · 324 citations
Neutrophils are the first responders to infection and inflammation and are thus a critical component of innate immune defense. Understanding the behavior of neutrophils as they act within various i...
Cutting Edge: <i>Mycobacterium tuberculosis</i> Induces Aerobic Glycolysis in Human Alveolar Macrophages That Is Required for Control of Intracellular Bacillary Replication
Laura E. Gleeson, Frederick J. Sheedy, Eva M. Pålsson‐McDermott et al. · 2016 · The Journal of Immunology · 305 citations
Abstract Recent advances in immunometabolism link metabolic changes in stimulated macrophages to production of IL-1β, a crucial cytokine in the innate immune response to Mycobacterium tuberculosis....
Reading Guide
Foundational Papers
No pre-2015 papers available; start with Netea et al. (2020, 2299 citations) for core definitions and Gleeson et al. (2016, 305 citations) for glycolysis in TB macrophages.
Recent Advances
Ochando et al. (2022, 347 citations) on immunopathology; Wang et al. (2021, 237 citations) on mitochondrial regulation; Chandra et al. (2022, 509 citations) on TB evasion.
Core Methods
Metabolomics and flux analysis (Gleeson et al., 2016); Seahorse respirometry (Wang et al., 2021); monocyte phenotyping by flow cytometry (Kapellos et al., 2019); epigenetic profiling (van der Heijden et al., 2017).
How PapersFlow Helps You Research Immunometabolism in Trained Immunity
Discover & Search
Research Agent uses searchPapers and exaSearch to find papers on 'glycolysis in trained immunity macrophages', surfacing Gleeson et al. (2016). CitationGraph reveals Netea et al. (2020, 2299 citations) as a hub connecting to van der Heijden et al. (2017) and Groh et al. (2017); findSimilarPapers expands to monocyte immunometabolism clusters.
Analyze & Verify
Analysis Agent applies readPaperContent to extract metabolic pathways from Gleeson et al. (2016), then runPythonAnalysis with pandas to quantify glycolysis gene expression shifts across Netea et al. (2020) datasets. VerifyResponse via CoVe cross-checks claims against Kapellos et al. (2019), with GRADE grading for evidence strength in TB control.
Synthesize & Write
Synthesis Agent detects gaps in glycolysis-epigenetics links between van der Heijden et al. (2017) and Groh et al. (2017), flagging contradictions in monocyte subsets. Writing Agent uses latexEditText and latexSyncCitations to draft reviews, latexCompile for figures, and exportMermaid for pathway diagrams.
Use Cases
"Analyze glycolysis flux data from TB macrophage papers using Python."
Research Agent → searchPapers('glycolysis macrophages tuberculosis') → Analysis Agent → readPaperContent(Gleeson 2016) → runPythonAnalysis(pandas plot of metabolic rates) → matplotlib flux diagram output.
"Write LaTeX review on immunometabolism in trained immunity monocytes."
Synthesis Agent → gap detection(Netea 2020 + Groh 2017) → Writing Agent → latexEditText(draft section) → latexSyncCitations → latexCompile(PDF with figures) → exportBibtex.
"Find code for modeling trained immunity metabolic networks."
Research Agent → paperExtractUrls(van der Heijden 2017) → paperFindGithubRepo → githubRepoInspect → Code Discovery workflow outputs runnable Python scripts for glycolysis simulations.
Automated Workflows
Deep Research workflow scans 50+ papers via citationGraph from Netea et al. (2020), generating structured reports on metabolic shifts in monocytes. DeepScan applies 7-step CoVe analysis to Gleeson et al. (2016) vs. Wang et al. (2021), verifying TB glycolysis claims. Theorizer builds hypotheses on glutaminolysis modulation from Groh et al. (2017) and Kapellos et al. (2019).
Frequently Asked Questions
What defines immunometabolism in trained immunity?
It covers metabolic rewiring like aerobic glycolysis enabling sustained innate responses post-BCG training, as in Netea et al. (2020).
What methods study this subtopic?
Techniques include metabolomics on monocyte subsets (Kapellos et al., 2019), Seahorse assays for glycolysis (Gleeson et al., 2016), and ChIP-seq for epigenetic links (van der Heijden et al., 2017).
What are key papers?
Netea et al. (2020, 2299 citations) defines trained immunity; Gleeson et al. (2016, 305 citations) links glycolysis to TB control; Groh et al. (2017) covers atherosclerosis applications.
What open problems exist?
Challenges include causal metabolic-epigenetic links (van der Heijden et al., 2017), subset-specific responses (Kapellos et al., 2019), and human disease translation (Ochando et al., 2022).
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Part of the Immune responses and vaccinations Research Guide