1008

Transcriptomic Signatures of Brain Regions Vulnerable to Anatomical and Metabolic Changes in Temporal Lobe Epilepsy
Jiwei Li1, Hui Huang1, Bingyang Cai1, Siyu Yuan1, and Jie Luo1
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

Synopsis

Keywords: Epilepsy, Tissue Characterization

Imaging transcriptomics could bridge the gap between connectome and transcriptome. In this study, we selected brain regions that were vulnerable to hypometabolism, and those vulnerable to atrophy, then investigated transcriptional signatures and cell-type composition differences that may contribute to TLE-related brain structural and metabolic changes. We found hippocampus and entorhinal were found to be most vulnerable brain regions in both anatomical and metabolic changes in TLE patients. Enrichment analysis found that differential expression genes most significantly enriched in neuroactive ligand-receptor interaction pathway. Inhibitory neuron, microglia and oligodendrocyte precursor cells showed significant difference between vulnerable regions and relatively healthy regions.

Introduction

Mesial temporal lobe epilepsy (mTLE) is the most common type of drug refractory epilepsy1, which could benefit from imaging transcriptomics studies to elucidate underlying cellular and molecular mechanisms. Previous studies have made efforts in exploring transcriptional signatures corresponding to neuroanatomical change2, structural and functional coupling3 and structural network alteration4 in epilepsy, partially enabled by the open-access high resolution whole brain transcriptome datasets Allen human brain atlas (AHBA)5,6. Hypometabolism in mesial temporal brain regions as well as certain extratemporal regions is widely observed in FDG PET studies of mTLE7,8. In this study, we selected brain regions that are vulnerable to hypometabolism, and those vulnerable to atrophy, then investigated transcriptional signatures and cell-type composition differences that may contribute to TLE-related brain structural and metabolic changes.

Methods

Data acquisition
In this IRB approved study, we recruited 73 patients with a clinical diagnosis of unilateral mTLE and 55 healthy controls. Image data were collected on the Siemens PET/MR Synchronous integrated scanner (Biograph mMR), including T1-weighted MPRAGE images (TR/TE/TI = 1900/2.44/900 ms, resolution = 1.0 × 1.0 × 1.0 mm3, FOV = 256 × 256 mm2, 192 slices) and T2-weighted FLAIR (TR/TE/TI = 8460/92/2433 ms, resolution = 0.4 × 0.4 × 3.0 mm3, FOV = 220 × 220 mm2, 45 slices) for all subjects. The [18F] fluorodeoxyglucose-PET (FDG-PET) were obtained at 30~60 minutes post a bolus injection of 18F-FDG (mean dose of 3.7 MBq/kg, matrix size = 344 × 344, voxel size = 2.0 × 2.0 × 2.0 mm3, 127 slices) for all patients and 36 of healthy controls.

Imaging data processing
FreeSurfer v6.0.0 was used to obtain brain segmentations of T1-weighted images based on the Desikan-Killiany atlas (DK atlas)9. Cortical thickness and subcortical volumes were extracted to evaluate brain atrophy. 18F-FDG PET was registered to T1w and standardized uptake value ratio (SUVR) was determined to normalize intensities by using cerebellum gray matter as the reference. The SUVR of each ROI was extracted using DK atlas mask.
Mann-Whitney U test was used to compare ipsilateral brain regions in MR hippocampal sclerosis (MR-HS) and MR-negative groups to corresponding ROIs in healthy control group to obtain vulnerable regions from SUVR data and atrophy data respectively. Brain regions identified in both MR-HS and MR-negative groups are considered most vulnerable; brain regions identified in MR-HS only are considered moderately vulnerable, while the rest are considered relatively healthy to seizure related damages.

Transcriptome data analysis
AHBA was download from http://www.brain-map.org. We restricted our analysis to the left hemisphere, which are available for all subjects in AHBA.
For gene selection, microarray probes were re-annotated using data provided by Arnatkeviciute A et al10. And we calculated the mean of all available probes for a gene. The identification of differential expression genes (DEGs) between the two types vulnerable regions and relatively healthy regions was performed using Limma11 package in R v4.2.1 with a threshold of fold-change ≥ 2 and P≤0.05. Enrichment analysis was implemented in the following gene sets and gene ontologies: Gene and Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to identify cellular processes or pathways that may be enriched by the differential genes.
For differential cell-type analysis, we chose cell fractions of 3702 samples provided by Altmann A et al12 (https://github.com/andrealtmann/AHBA_Epilepsy/blob/master/data/celltype_deconvolution.txt). We excluded samples if they were not a designated cortical or subcortical sample or not mapped to our interested ROIs, or the closest ROI was more than 4 mm away. We used Mann-Whitney U test to compare vulnerable regions and relatively healthy regions to identify cell-types that were differently expressed.

Results

Participants demographics summarized in Table 1. In clinical evaluations, 38 patients were MR-HS and 35 patients were MR-negative. Atrophy was evaluated based on cortical thickness and subcortical volume changes between MR-HS, MR-negative TLE, and age- and gender-matched healthy control groups. Similarly, vulnerability to hypometabolism was evaluated based on FDG uptake. Remarkably, all regions found in MR-negative group were included in MR-HS groups. Hippocampus and entorhinal were identified as most vulnerable regions both in atrophy and in hypometabolism analysis (Figure 1 A, B).
After data preprocessing, we generated the expression matrix that contained 20,265 genes from 1,765 samples. The number of DEGs were 311 in most vulnerable regions of atrophy, 16 in moderately vulnerable regions of atrophy, 60 in most vulnerable regions of hypometabolism and only 1 in moderately vulnerable regions of hypometabolism. We only performed enrichment analysis for the DEGs from the most vulnerable regions, significant terms are shown in Figure 2.
The differential cell-type analysis showed a consistent pattern in most vulnerable regions both to atrophy and hypometabolism. The significant difference cell-types, inhibitory neuron, microglia, astrocytes and oligodendrocyte precursor cells (OPC) were elevated while excitatory neuron and oligodendrocytes were decreased (Figure 3).

Conclusions

Hippocampus and entorhinal were found to be most vulnerable brain regions in both anatomical and metabolic changes in TLE patients. Transcriptomic analysis revealed common findings in neuroactive ligand-receptor interaction pathway, which is generally consistent with cellular component changes.

Acknowledgements

N/A

References

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Figures

Table 1. Participants demographics. Data were represented as the mean ± standard deviation. a: P value was calculated using Mann-Whitney U test; b: P value was calculated using chi-square test. Abbreviations: TLE, Temporal lobe epilepsy; HC, healthy controls; L, left; R, right; M, male; F; female.

Figure 1. Venn diagram of selected vulnerable regions. A) Vulnerable brain regions of MR-HS and MR-negative groups from atrophy data. B) Vulnerable brain regions of MR-HS and MR-negative groups from SUVR data. C) The relationship of most and moderately vulnerable regions in atrophy data and SUVR data.

Figure 2. Enrichment analysis of differential expression genes of atrophy (A) and hypometabolism (B). DEGs which identified between most vulnerable regions and relatively healthy regions were enriched in GO terms (left) and in KEGG pathways (right). KEGG pathway map showing change of “neuroactive ligand-receptor interaction” (bottom). DEGs with relatively increased and reduced expression were shown in red and blue, respectively, while green represented background genes.

Figure 3. Brain regions selection and cell-type analysis. A - C) Results of atrophy data; D - F) Results of SUVR data. A, D) Most vulnerable regions, moderately vulnerable regions and relatively healthy regions were plotted on brain hemisphere surface. B, E) Probability distribution curve of most vulnerable regions and relatively healthy regions. C, F) Probability distribution curve of moderately vulnerable regions and relatively healthy regions. All cell-type fractions were between 0 and 1. (*** 1e-20≤ PFDR < 1e-10; ** 1e-10 ≤ PFDR < 1e-5; * 1e-5≤ PFDR < 0.001)

Proc. Intl. Soc. Mag. Reson. Med. 31 (2023)
1008
DOI: https://doi.org/10.58530/2023/1008