CSF amino acid profiles in ICV-streptozotocin-induced sporadic Alzheimer's disease in male Wistar rat: a metabolomics and systems biology perspective

Abstract

Alzheimer's disease (AD) is an increasingly important public health concern due to the increasing proportion of older individuals within the general population. The impairment of processes responsible for adequate brain energy supply primarily determines the early features of the aging process. Restricting brain energy supply results in brain hypometabolism prior to clinical symptoms and is anatomically and functionally associated with cognitive impairment. The present study investigated changes in metabolic profiles induced by intracerebroventricular-streptozotocin (ICV-STZ) in an AD-like animal model. To this end, male Wistar rats received a single injection of STZ (3 mg·kg^-1) by ICV (2.5 μL into each ventricle for 5 min on each side). In the second week after receiving ICV-STZ, rats were tested for cognitive performance using the Morris Water Maze test and subsequently prepared for positron emission tomography (PET) to confirm AD-like symptoms. Tandem Mass Spectrometry (MS/MS) analysis was used to detect amino acid changes in cerebrospinal fluid (CFS) samples. Our metabolomics study revealed a reduction in the concentrations of various amino acids (alanine, arginine, aspartic acid, glutamic acid, glycine, isoleucine, methionine, phenylalanine, proline, serine, threonine, tryptophane, tyrosine, and valine) in CSF of ICV-STZ-treated animals as compared to controls rats. The results of the current study indicate amino acid levels could potentially be considered targets of nutritional and/or pharmacological interventions to interfere with AD progression.

Keywords: glucose metabolism; neurometabolic; sporadic Alzheimer's Disease; streptozotocin; systems biology.

Fig. 1

Schematic diagram of the experimental design.

Fig. 2

Assessment of learning and memory (Morris water maze test) in sham‐ and ICV‐STZ‐treated male rats. All animals were trained to find a hidden platform zone for 3 days. Then, a probe test was performed to determine the learning capacity of groups. (A) A Two‐way ANOVA of scape latency (the time to find the hidden platform) during the training phase in the ICV‐STZ compared to the sham group. (B) A comparison of the average heat map of MWM trials between groups. The heat map scale bar indicates the time in seconds (C) Student's t‐test analysis of scape latency to platform shows ICV‐STZ‐induced rats spent more time finding the hidden platform at the former location of the platform. (D) Student's t‐test analysis of time in the target zone shows ICV‐STZ‐induced rats spent less time in the target quadrant searching for the missing than did the sham group. (**P < 0.01, ***P < 0.001, and ****P < 0.0001 compared to the sham group; n = 10 rats in each group). Data are presented as mean ± SEM. ICV‐STZ, Intracerebroventricular‐streptozotocin.

Fig. 3

Changes in brain glucose metabolism in microPET images. (A) Student's t‐test analysis of the brain radionuclide uptake ratio value in the ICV‐STZ compared to the sham group. Rats were each injected via the tail vein with about 800 μCi of the ¹⁸FDG under general anesthesia. For each small‐animal PET scan, 3‐dimensional regions of interest (ROIs) were manually drawn around the brain, and the backgrounds were selected behind the backbone. The ROIs were converted to the brain‐to‐background ratio (BBR) as (ROI counts per voxel)/(background counts per voxel). (B, C) The amount of the radionuclide uptake ratio of the selected ROIs for 1 h PET scan of each rat. The small‐animal PET scans were obtained with a microPET scanner (Xtrim PET) at the Preclinical Core Facility (TPCF) based at Tehran University of Medical Sciences. (**P < 0.01 compared to sham group; n = 4 rats in each group.) Data are presented as mean ± SEM. BBR, Brain‐to‐background ratio; FDG, Fludeoxyglucose F18; PET, A positron emission tomography; ROIs, region of interest.

Fig. 4

KEGG and SMPDB enrichment analysis. Results using the KEGG (A) and SMPDB (B) databases rendered 9 and 7 pathways, respectively. We used a FDR threshold of less than 0.05 to determine which pathways were significantly affected. FDR, False discovery rate.

Fig. 5

Diagram showing the relationship between genes and metabolites related to Alzheimer's disease. Eight genes (SLC16A10, SLC7A8, SLC6A19, IL4l1, SLC6A14, IARS, SLC38A2, and SLC6A15) are linked with significant metabolites that are pertinent to Alzheimer.