Association of CNR1 gene and cannabinoid 1 receptor protein in the human brain

We aimed to integrate genomic mapping from brain mRNA atlas with the protein expression from positron emission tomography (PET) scans of type 1 cannabinoid (CB1) receptor and to compare the predictive power of CB1 receptor with those of other neuroreceptor/transporters using a meta-analysis. Volume of distribution (V-T) from F18-FMPEP-d2 PET scans, CNR1 gene (Cannabinoid receptor 1) expression, and H3-CP55940 binding were calculated and correlation analysis was performed. Between V-T of F18-FMPEP-d2 PET scans and CNR1 mRNA expression, moderate strength of correlation was observed (rho = .5067, p = .0337). Strong positive correlation was also found between CNR1 mRNA expression and H3-CP55940 binding (r = .6336, p = .0364), validating the finding between F18-FMPEP-d2 PET scans and CNR1 mRNA. The correlation between V-T of F18-FMPEP-d2 PET scans and H3-CP55940 binding was marginally significant (r = .5025, p = .0563). From the meta-analysis, the correlation coefficient between mRNA expression and protein expressions ranged from -.10 to .99, with a pooled effect of .76. In conclusion, we observed the moderate to strong associations between gene and protein expression for CB1 receptor in the human brain, which was validated by autoradiography. We combined the autoradiographic finding with PET of CB1 receptor, producing the density atlas map of CB1 receptor. From the meta-analysis, the moderate to strong correlation was observed between mRNA expression and protein expressions across multiple genes. Further study is needed to investigate the relationship between multiple genes and in vivo proteins to improve and accelerate drug development

APOE mediated neuroinflammation and neurodegeneration in Alzheimer\u27s disease

Neuroinflammation is a central mechanism involved in neurodegeneration as observed in Alzheimer\u27s disease (AD), the most prevalent form of neurodegenerative disease. Apolipoprotein E4 (APOE4), the strongest genetic risk factor for AD, directly influences disease onset and progression by interacting with the major pathological hallmarks of AD including amyloid-β plaques, neurofibrillary tau tangles, as well as neuroinflammation. Microglia and astrocytes, the two major immune cells in the brain, exist in an immune-vigilant state providing immunological defense as well as housekeeping functions that promote neuronal well-being. It is becoming increasingly evident that under disease conditions, these immune cells become progressively dysfunctional in regulating metabolic and immunoregulatory pathways, thereby promoting chronic inflammation-induced neurodegeneration. Here, we review and discuss how APOE and specifically APOE4 directly influences amyloid-β and tau pathology, and disrupts microglial as well as astroglial immunomodulating functions leading to chronic inflammation that contributes to neurodegeneration in AD

Differential patterns of gray matter volumes and associated gene expression profiles in cognitively-defined Alzheimer's disease subgroups

The clinical presentation of Alzheimer’s disease (AD) varies widely across individuals but the neurobiological mechanisms underlying this heterogeneity are largely unknown. Here, we compared regional gray matter (GM) volumes and associated gene expression profiles between cognitively-defined subgroups of amyloid-β positive individuals clinically diagnosed with AD dementia (age: 66 ± 7, 47% male, MMSE: 21 ± 5). All participants underwent neuropsychological assessment with tests covering memory, executive-functioning, language and visuospatial-functioning domains. Subgroup classification was achieved using a psychometric framework that assesses which cognitive domain shows substantial relative impairment compared to the intra-individual average across domains, which yielded the following subgroups in our sample; AD-Memory (n = 41), AD-Executive (n = 117), AD-Language (n = 33), AD-Visuospatial (n = 171). We performed voxel-wise contrasts of GM volumes derived from 3Tesla structural MRI between subgroups and controls (n = 127, age 58 ± 9, 42% male, MMSE 29 ± 1), and observed that differences in regional GM volumes compared to controls closely matched the respective cognitive profiles. Specifically, we detected lower medial temporal lobe GM volumes in AD-Memory, lower fronto-parietal GM volumes in AD-Executive, asymmetric GM volumes in the temporal lobe (left < right) in AD-Language, and lower GM volumes in posterior areas in AD-Visuospatial. In order to examine possible biological drivers of these differences in regional GM volumes, we correlated subgroup-specific regional GM volumes to brain-wide gene expression profiles based on a stereotactic characterization of the transcriptional architecture of the human brain as provided by the Allen human brain atlas. Gene-set enrichment analyses revealed that variations in regional expression of genes involved in processes like mitochondrial respiration and metabolism of proteins were associated with patterns of regional GM volume across multiple subgroups. Other gene expression vs GM volume-associations were only detected in particular subgroups, e.g., genes involved in the cell cycle for AD-Memory, specific sets of genes related to protein metabolism in AD-Language, and genes associated with modification of gene expression in AD-Visuospatial. We conclude that cognitively-defined AD subgroups show neurobiological differences, and distinct biological pathways may be involved in the emergence of these differences

Regional expression profiles of risk genes for depression are associated with brain activation patterns in emotion and reward tasks

The exploration of the relationship between gene expression profiles and neural response patterns known to be altered in major depressive disorder provides a unique opportunity to identify novel targets for diagnosis and therapy. Here, we estimated the spatial association between genome-wide transcriptome maps and brain activation patterns from functional magnetic resonance imaging (fMRI) with two established paradigms of great relevance for mood disorders. While task-specific neural responses during emotion processing were primarily associated with expression patterns of genes involved in cellular transport, reward processing was related to neuronal development, synapse regulation, as well as gene transcription. Multimodal integration of single-site and meta-analytic imaging data with risk genes associated with depression revealed a regional susceptibility of functional activity, modulated by master regulators TCF4 and MEF2C. The identification of multiple subordinate genes correlated with fMRI maps and their corresponding regulators presumably will reshape the prospects for neuroimaging genetics. ONE SENTENCE SUMMARY: Analysis of the spatial association between whole-brain human gene expression and in-vivo brain activation patterns during emotion and reward processing identified TCF4 and MEF2C as master regulatory genes associated with depressive disorders

The Molecular-enriched Functional Circuits Underlying Consciousness and Cognition

Homo Sapiens consist of trillions of atoms, each inanimate, yet somehow collectively constituting a conscious being. The fundamental question of how organisms are organised to beget consciousness and cognition has largely been approached through independent examination of the structure and function of the nervous system at varying levels of granularity. As neuroscience progresses, it has thus increasingly fragmented into separate streams of research which study the brain at these different scales. This has resulted in the field becoming “data rich, but theory poor”, which is largely attributable to the paucity of methods which bridge these levels of analysis to provide novel trans-hierarchical insights and inform unified theories. The research in this doctoral thesis therefore aims to explore how a specific type of multimodal analysis - Receptor-Enriched Analysis of functional Connectivity by Targets (REACT) – can begin to bridge the theoretic void between molecular level mechanisms and systems levels dynamics to provide novel perspectives on the function and dysfunction of the brain. First, I provide a narrative synthesis of the challenges precluding a meaningful understanding of the human brain utilising conventional functional neuroimaging and outlining how incorporation of molecular information may help overcome these limitations. Specifically, by embedding functional dynamics in the molecular landscape of the brain, we can begin to move from the simple characterisation of “where” cognitive phenomena may be within the brain towards mechanistic accounts of “how” they are produced. Additionally, this offers enticing opportunities to link pharmacological treatments to novel molecular-network based biomarkers. Second, I explore how networks enriched with the spatial configurations of serotonergic and dopaminergic receptor subtypes are modulated by lysergic acid diethylamide (LSD) as compared to placebo in healthy participants. The results highlight the challenges of disentangling pharmacodynamics of drugs exhibiting rich pharmacology as well as identifying differential relationship between serotonergic and dopaminergic networks and phenomenological sub- components of psychedelic state. Third, I expand the remit of molecular-enriched network analyses beyond pure psychopharmacology to examine the direct and indirect actions of propofol anaesthesia on inhibitory and modulatory neurotransmission at both rest as well as during a naturalistic listening task. This work demonstrates for the first time that these molecular-networks can capture broader perceptual and cognitive-driven network reconfigurations as well as indirect pharmacological actions on neuromodulatory systems. Moreover, it provides evidence that the effects of propofol on consciousness are enacted through both direct inhibitory as well as indirect neuromodulatory mechanisms.Finally, I produce normative models of networks enriched with the principal neuromodulatory, excitatory, and inhibitory transmitter systems, testing their capacity to characterise neural dysfunction within and across several neuropsychiatric disorders. This work provides a computational foundation for large scale integration of molecular mechanisms and functional imaging to provide novel individualised biomarkers for neuropsychiatric disorders. Collectively, this thesis offers methodological and theoretical progress towards a trans-hierarchical characterisation of the human brain, providing insights into the neural correlates of both conscious contents and level as well as the perturbations underlying key neuropsychiatric conditions