Predictive metabolic networks reveal sex‐ and <i>APOE</i> genotype‐specific metabolic signatures and drivers for precision medicine in Alzheimer's disease

Chang, Rui; Trushina, Eugenia; Zhu, Kuixi; Zaidi, Syed Shujaat Ali; Lau, Branden M.; Kueider‐Paisley, Alexandra; Moein, Sara; He, Qianying; Alamprese, Melissa L.; Vagnerova, Barbora; Tang, Andrew; Vijayan, Ramachandran; Liu, Yanyun; Saykin, Andrew J.; Brinton, Roberta D.; Kaddurah‐Daouk, Rima; ,

doi:10.1002/alz.12675

Cited by 24 publications

(11 citation statements)

References 51 publications

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“…Lipidomic analyses of the postmortem human AD brain have noted changes in brain lipids during the course of the disease. 66 Recent work extends these findings to include E4-associated decreases in several phospholipid species 67 , 68 and isoform-specific microglial responses to ApoE-containing, phospholipid-rich lipoproteins. 69 Although brain lipidomic profiling typically relies on tissue homogenates from preselected regions, we here leveraged MALDI MSI to simultaneously quantify multiple lipid classes across entire intact brain tissue sections.…”

Section: Discussionmentioning

confidence: 76%

APOE modulates microglial immunometabolism in response to age, amyloid pathology, and inflammatory challenge

Lee¹,

Devanney²,

Golden³

et al. 2023

Cell Reports

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Section: Discussionmentioning

confidence: 76%

APOE modulates microglial immunometabolism in response to age, amyloid pathology, and inflammatory challenge

Lee¹,

Devanney²,

Golden³

et al. 2023

Cell Reports

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“…Predictive network modeling was performed according to detailed methods described in our recent publications 23 , 59 , 60 , 199 , 200 as well as methodology patent US11068799B2.…”

Section: Methodsmentioning

confidence: 99%

Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer’s disease

et al. 2023

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Despite decades of genetic studies on late-onset Alzheimer’s disease, the underlying molecular mechanisms remain unclear. To better comprehend its complex etiology, we use an integrative approach to build robust predictive (causal) network models using two large human multi-omics datasets. We delineate bulk-tissue gene expression into single cell-type gene expression and integrate clinical and pathologic traits, single nucleotide variation, and deconvoluted gene expression for the construction of cell type-specific predictive network models. Here, we focus on neuron-specific network models and prioritize 19 predicted key drivers modulating Alzheimer’s pathology, which we then validate by knockdown in human induced pluripotent stem cell-derived neurons. We find that neuronal knockdown of 10 of the 19 targets significantly modulates levels of amyloid-beta and/or phosphorylated tau peptides, most notably JMJD6. We also confirm our network structure by RNA sequencing in the neurons following knockdown of each of the 10 targets, which additionally predicts that they are upstream regulators of REST and VGF. Our work thus identifies robust neuronal key drivers of the Alzheimer’s-associated network state which may represent therapeutic targets with relevance to both amyloid and tau pathology in Alzheimer’s disease.

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“… 698 Based on 127 metabolic signatures from the Alzheimer’s Disease, specific metabolic networks modeling for diagnosis were constructed and provided key insights for personalized late-onset. 699 Recently, Guo et al performed a metabolic network-based identification modeling for mapping the differential metabolites, a panel of eight candidate metabolites (i.e., palmitic acid, pyruvate, tryptophan) were further indicated a high discrimination for non-small cell lung cancer (accuracy > 97.7%). 700 …”

Section: Metabolic Network Explorationmentioning

confidence: 99%

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Qiu

Cai

et al. 2023

Sig Transduct Target Ther

240

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Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject’s phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

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Predictive metabolic networks reveal sex‐ and APOE genotype‐specific metabolic signatures and drivers for precision medicine in Alzheimer's disease

Cited by 24 publications

References 51 publications

APOE modulates microglial immunometabolism in response to age, amyloid pathology, and inflammatory challenge

APOE modulates microglial immunometabolism in response to age, amyloid pathology, and inflammatory challenge

Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer’s disease

Small molecule metabolites: discovery of biomarkers and therapeutic targets

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