STM｜Xianyuan Xiang and others revealed that microglia-mediated inflammation changes brain energy metabolism and helps early diagnosis of neurodegenerative diseases

EditorEnzyme Glucose is the main fuel for brain cells
.

The human brain accounts for about 2% of the total body weight, but it consumes 20% of the energy of the body's glucose source
.

In neurodegenerative diseases, such as Alzheimer's disease, there are complex changes in brain energy metabolism [1]
.

18F-labeled fluorodeoxyglucose positron emission tomography (glucose-PET) is widely used to detect brain glucose uptake, thereby indirectly measuring the degree of neuronal damage [2]
.

However, glucose-PET lacks cell resolution and cannot distinguish the cell types that contribute to the PET signal
.

A large number of previous studies have pointed out that the synaptic activity of the nerve is the main source of the glucose-PET signal.
Therefore, clinically, the strength of the glucose-PET signal is directly linked to the neural activity [3-5]
.

However, in the early stage of accumulation of the pathological molecule b-amyloid (Ab) in Alzheimer's disease, the patient's glucose-PET signal showed a short-term regional increase [6-8]
.

The reason and mechanism of the regional increase in brain glucose uptake in the early stage of such diseases have not yet been elucidated
.

On October 13, 2021, the research group of Matthias Brendel and Christian Haass of the University of Munich (the first author is Dr.
Xiang Xianyuan) published an online publication on Science Translational Medicine entitled Microglial activation states drive glucose uptake and FDG-PET alterations in neurodegenerative diseases The article, amended people’s understanding of brain glucose-PET imaging, and assisted in the early diagnosis of neurodegenerative diseases
.

This article states that the glucose-PET signal is directly affected by the glucose uptake of immune cells in the brain.
The activity state of the immune cells in the brain is the cause of the early changes in the glucose-PET signal in patients
.

In this study, the authors analyzed whether glucose-PET imaging results are directly affected by the glucose uptake of microglia in mouse models and patients with neurodegenerative diseases
.

When 18F-labeled fluorodeoxyglucose is injected into the body, fluorodeoxyglucose will be quickly absorbed and stay in the cell, and its radioactive signal can be detected, thereby reflecting the glucose uptake capacity of the cell or tissue
.

After injecting this type of glucose into different mouse models, the author sorted out neurons, astrocytes and microglia (the main immune cells in the brain), and detected the radioactivity released by glucose in various cells.
Signal to determine that the glucose uptake of microglia is higher than that of neurons and astrocytes
.

When microglia lack TREM2 (trigger receptor 2, a gene involved in metabolism and activation of microglia) expressed on myeloid cells, their glucose uptake capacity is significantly reduced, so mice lacking TREM2 show lower Glucose-PET signal
.

The author collected a cohort of patients with Alzheimer's disease and Tau protein disease, and performed glucose-PET imaging and microglia activity-PET (TSPO-PET) imaging
.

In brain areas where there is no obvious nerve damage but pathological changes have occurred, glucose-PET and TSPO-PET signals are significantly positively correlated, suggesting that the activity of microglia in patients can also directly affect glucose-PET signals
.

Therefore, data from animal models and clinical patients indicate that the immune cells in the brain, microglia, are characterized by large amounts of glucose uptake
.

These findings directly changed how we interpret the results of glucose-PET imaging clinically
.

It is important for clinicians to understand the source of the image signal
.

Figure: The degree of activation of microglia determines its glucose uptake, which affects the patient's glucose-PET signal
.

The understanding that the glucose PET signal in the brain is almost entirely determined by the function of neurons needs to be corrected, because the inflammatory response mediated by microglia has a crucial influence on the glucose uptake in the brain
.

This research is of great significance for correctly interpreting glucose-PET data and guiding early diagnosis of neurodegenerative diseases
.

Attachment: Postdoctoral recruitment of Professor Helmut Kettenmann's team at the Brain Function and Atlas Research Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences-Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (siat.
ac.
cn), please see Kettenmann Lab (x-mol.
com) Original link: https:// Platemaker: 11 References 1.
CR Jack, Jr.
et al.
, Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers.
Lancet Neurol 12, 207-216 (2013).
2.
CC Tang et al.
, Differential diagnosis of parkinsonism: a metabolic imaging study using pattern analysis.
Lancet Neurol 9, 149-158 (2010) 3.
L.
Sokoloff et al.
, The [14C]deoxyglucose method for the measurement of local cerebral glucose utilization: theory, procedure, and normal values ​​in the conscious and anesthetized albino rat.
J Neurochem 28, 897-916 (1977).
4.
L.
Sokoloff, Energetics of functional activation in neural tissues.
Neurochem Res 24, 321-329 (1999).
5.
L.
Sokoloff,Sites and mechanisms of function-related changes in energy metabolism in the nervous system.
Dev Neurosci 15, 194-206 (1993).
6.
H.
Oh, C.
Habeck, C.
Madison, W.
Jagust, Covarying alterations in Abeta deposition , glucose metabolism, and gray matter volume in cognitively normal elderly.
Hum Brain Mapp 35, 297-308 (2014).
7.
BA Gordon et al.
, Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer's disease: a longitudinal study.
Lancet Neurol 17, 241-250 (2018).
8.
TL Benzinger et al.
, Regional variability of imaging biomarkers in autosomal dominant Alzheimer's disease.
Proc Natl Acad Sci USA 110, E4502-4509 (2013).
Instructions for reprinting【 Non-original articles] The copyright of this article belongs to the author of the article.
Personal forwarding and sharing are welcome.
Reprinting is prohibited without permission.
The author has all legal rights, and offenders must be investigated.
194-206 (1993).
6.
H.
Oh, C.
Habeck, C.
Madison, W.
Jagust, Covarying alterations in Abeta deposition, glucose metabolism, and gray matter volume in cognitively normal elderly.
Hum Brain Mapp 35, 297- 308 (2014).
7.
BA Gordon et al.
, Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer's disease: a longitudinal study.
Lancet Neurol 17, 241-250 (2018).
8.
TL Benzinger et al .
, Regional variability of imaging biomarkers in autosomal dominant Alzheimer's disease.
Proc Natl Acad Sci USA 110, E4502-4509 (2013).
Reprinting instructions [Non-original articles] The copyright of this article belongs to the author of the article.
Personal forwarding and sharing are welcome, and it is prohibited without permission Reprinted, the author has all legal rights, offenders must be investigated194-206 (1993).
6.
H.
Oh, C.
Habeck, C.
Madison, W.
Jagust, Covarying alterations in Abeta deposition, glucose metabolism, and gray matter volume in cognitively normal elderly.
Hum Brain Mapp 35, 297- 308 (2014).
7.
BA Gordon et al.
, Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer's disease: a longitudinal study.
Lancet Neurol 17, 241-250 (2018).
8.
TL Benzinger et al .
, Regional variability of imaging biomarkers in autosomal dominant Alzheimer's disease.
Proc Natl Acad Sci USA 110, E4502-4509 (2013).
Reprinting instructions [Non-original articles] The copyright of this article belongs to the author of the article.
Personal forwarding and sharing are welcome, and it is prohibited without permission Reprinted, the author has all legal rights, offenders must be investigatedGordon et al.
, Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer's disease: a longitudinal study.
Lancet Neurol 17, 241-250 (2018).
8.
TL Benzinger et al.
, Regional variability of imaging biomarkers in Autosomal dominant Alzheimer's disease.
Proc Natl Acad Sci USA 110, E4502-4509 (2013).
Reprinting instructions [Non-original articles] The copyright of this article belongs to the author of the article.
Personal forwarding and sharing are welcome.
Reprinting is prohibited without permission.
The author has all legal rights.
Offenders must be investigatedGordon et al.
, Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer's disease: a longitudinal study.
Lancet Neurol 17, 241-250 (2018).
8.
TL Benzinger et al.
, Regional variability of imaging biomarkers in Autosomal dominant Alzheimer's disease.
Proc Natl Acad Sci USA 110, E4502-4509 (2013).
Reprinting instructions [Non-original articles] The copyright of this article belongs to the author of the article.
Personal forwarding and sharing are welcome.
Reprinting is prohibited without permission.
The author has all legal rights.
Offenders must be investigated
.