Distinct Basal Metabolism in Three Mouse Models of Neurodevelopmental Disorders

Menzies, Caitlin; Naz, Saima; Patten, David A.; Alquier, Thierry; Bennett, Brian M.; Lacoste, Baptiste

doi:10.1523/eneuro.0292-20.2021

Cited by 17 publications

(16 citation statements)

References 49 publications

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“…Clinical studies have found mitochondrial and metabolic dysfunction or changes in metabolites in primary lymphocytes or brain tissue in individuals with ASD, but whether this is direct or indirect is not known (Anitha et al, 2012; Frye and Rossignol, 2011; Frye et al, 2013; Kurochkin et al, 2019; Rose et al, 2014, 2018; Rossignol and Frye, 2012; Wang et al, 2016b). Some ASD-associated syndromic disorders, co-morbid disorders and genetic ASD models have shown deficits in mitochondrial and metabolic processes, however the specific proteins involved were unknown (Bülow et al, 2021; Ebrahimi-fakhari et al, 2016; Fernandez et al, 2019; Jagtap et al, 2019; Kanellopoulos et al, 2020; Li et al, 2019; Licznerski et al, 2020; Madison et al, 2021; Menzies et al, 2021; Shen et al, 2019b; Shulyakova et al, 2017). Our findings indicate that TCA cycle and mitochondrial activity proteins are interacting with multiple ASD-risk genes.…”

Section: Resultsmentioning

confidence: 99%

Neuron-specific protein network mapping of autism risk genes identifies shared biological mechanisms and disease relevant pathologies

Murtaza

Cheng

Brown

et al. 2022

Preprint

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Autism spectrum disorder (ASD) is a genetically heterogeneous disorder. Sequencing studies have identified hundreds of risk genes for autism spectrum disorder (ASD), but the signaling networks of genes at the protein level remain largely unexplored, which can provide insight into previously unknown individual and convergent disease pathways in the brain. To address this gap, we used neuron-specific proximity-labeling proteomics (BioID) to identify protein-protein interaction (PPI) networks of 41 ASD-risk genes. Network analysis revealed the combined 41 risk gene PPI network map had more shared connectivity between unrelated ASD-risk genes than represented in existing public databases. We identified common pathways between established and uncharacterized risk genes, including synaptic transmission, mitochondrial/metabolic processes, Wnt signaling pathways, ion channel activity and MAPK signaling. Investigation of the mitochondrial and metabolic network using gene knockouts revealed a functional hub in neurons for multiple risk genes not previously associated with this pathway. Further, we identified that the uncharacterized ASD-risk gene PPP2R5D localizes to the synapse, which is disrupted by patient de novo missense mutations. Investigation of de novo missense variants of additional synaptic ASD-risk genes demonstrated that changes in PPI networks can capture synaptic transmission deficits. The neuronal 41 ASD-risk gene PPI network map also revealed enrichment for an additional 112 ASD-risk genes and human brain cell types implicated in ASD pathology. Interestingly, clustering of ASD-risk genes based on their PPI network connectivity identified multiple gene groups that correlate mutation-type to clinical behavior scores. Together, our data reveal that using PPI networks to map ASD risk genes can identify previously unknown individual and convergent neuronal signaling networks, provide a method to assess the impact of patient variants, and reveal new biological insight into disease mechanisms.

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Section: Resultsmentioning

confidence: 99%

Neuron-specific protein network mapping of autism risk genes identifies shared biological mechanisms and disease relevant pathologies

Murtaza

Cheng

Brown

et al. 2022

Preprint

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“…Plasmatic metabolites suggest a deregulation in glucose and lipid homeostasis, with a shifted metabolism towards the use of lipids and increased response to insulin stimulation [114] . Several studies report an increase in creatine and in BCAAs, asparagine and phosphocholine metabolism as well [ 114 , 115 ].…”

Section: Energy Metabolism In Neurodevelopmental Diseasesmentioning

confidence: 99%

Energy metabolism in childhood neurodevelopmental disorders

Oyarzábal

Musokhranova

et al. 2021

eBioMedicine

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Whereas energy function in the aging brain and their related neurodegenerative diseases has been explored in some detail, there is limited knowledge about molecular mechanisms and brain networks of energy metabolism during infancy and childhood. In this review we describe current insights on physiological brain energetics at prenatal and neonatal stages, and in childhood. We then describe the main groups of inborn errors of energy metabolism affecting the brain. Of note, scarce basic neuroscience research in this field limits the opportunity for these disorders to provide paradigms of energy utilization during neurodevelopment. Finally, we report energy metabolism disturbances in well-known non-metabolic neurodevelopmental disorders. As energy metabolism is a fundamental biological function, brain energy utilization is likely altered in most neuropediatric diseases. Precise knowledge on mechanisms of brain energy disturbance will open the possibility of metabolic modulation therapies regardless of disease etiology.

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“…It remains unclear whether alterations in these metabolic parameters are the result of specific genetic contexts or lifestyles, or whether altered gene expression due to trisomy 21 in peripheral tissues is causally linked to whole-body metabolic changes seen in DS. Despite the availability of DS mouse models in the past two decades, only a few recent studies reported on how aneuploidy alters baseline (chow-fed) metabolic parameters, and the in vivo response to obesogenic high-fat diet (HFD) [ [47] , [48] , [49] , [50] ]. A systematic and comprehensive assessment of glucose and lipid metabolism, and other key metabolic parameters, across different physiological states is currently lacking.…”

Section: Introductionmentioning

confidence: 99%