Yong Kim1,2*
Byung-Chang Suh3*- 1Department of Neurosurgery, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, United States
- 2Brain Health Institute, Rutgers University, Piscataway, NJ, United States
- 3Department of Brain Sciences, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, South Korea
Editorial on the Research Topic
Brain cells' compensatory mechanisms in response to disease risk factors
Our brain is highly plastic not only to sensory stimuli but also to environmental, chemical, and biological stressors. Molecules in brain cells must be altered and adapted in response to external challenges to maintain stability at the circuit and network levels and to behaviorally cope with external stressors or challenges. Similar adaptations are likely required in response to risk factors of brain disorders.
Brain plasticity or adaptation has been observed in response to stressful experiences (McEwen and Gianaros, 2011). Behavioral experience such as motor experience significantly affects the recovery of brain in either adaptive or maladaptive ways after brain injury (Nudo, 2013). Mechanical stress, i.e., traumatic brain injury causes multiple biochemical and cellular changes including intracellular trafficking, protein aggregation and complement activation (Surgucheva et al., 2014; Ng and Lee, 2019). In case of cancer therapy, intracellular adaptations of tumors or their adaptations to extracellular environment may lead to resistance against cancer drugs, resulting in transient or partial inhibition of tumor cell growth (Vaupel and Harrison, 2004; von Manstein et al., 2013). Maladaptation of brain reward system is implicated in drug addiction or persistent vulnerability to relapse (Koob and Le Moal, 2001; Ferland et al., 2019). Increased neuronal activity or hypermetabolism has been thought as a compensatory mechanism of neurodegeneration in Alzheimer's disease or Parkinson's disease (Ashraf et al., 2015; Blesa et al., 2017). In this regard, individual differences in molecular and cellular adaptations possibly drive susceptibility or resilience in response to stressors or risk factors of diseases as well as subsequent disease progression and/or vulnerability to relapse. Thus, studies of such compensatory mechanisms would provide a great opportunity of identifying disease mechanisms, new biomarkers and therapeutic targets.
Bhatti et al. used a chronic social defeat stress (CSDS) paradigm and searched critical cell types and molecular alterations involved in individual differences in stress responses in mice. They found parvalbumin (PV)-expressing GABAergic interneurons are altered in response to CSDS and their alterations are causally related to susceptibility or resilience to stress-induced social avoidance or anhedonia-like behavior. PV neuron-selective translational profiling indicates mitochondrial oxidative phosphorylation is the most significantly altered pathway in stress-susceptible versus resilient mice. Among differentially expressed genes associated with stress-susceptibility and resilience, the authors found alterations of Ahnak gene expression is causally related to stress-induced divergent behavioral adaptations. Notably, Ahnak was found as a major scaffolder of S100a10 and AnxA2 in the brain (Jin et al., 2020), and alterations of S100a10 is highly implicated in the pathophysiology of major depressive disorders and antidepressant actions (Svenningsson et al., 2013; Chen et al., 2022). Ahnak was also found as an endogenous regulator of L-type voltage-gated calcium channels (VGCCs) in the brain (Jin et al., 2020) and human genetic studies implicate altered function of L-type VGCCs in the pathophysiology of multiple psychiatric disorders including major depressive disorder, bipolar disorder, schizophrenia and autism spectrum disorder (Green et al., 2010; Liu et al., 2011; Bhat et al., 2012; Cross-Disorder Group of the Psychiatric Genomics [Corporate Author], 2013; Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014; Pinggera et al., 2015). Thus, their findings might be relevant to the pathophysiology of neuropsychiatric disorders.
Autism spectrum disorder (ASD), as a neurodevelopmental and neuropsychiatric disorder, is characterized by impaired social communication, restricted interests and elevated repetitive behaviors (Lord et al., 2018, 2020). Because ASD is affected by multigenic traits, genetic polymorphism in multiple genes in affected individuals may influence resilience or susceptibility to ASD (Bourgeron, 2015). Lim, Yoon et al. reviewed ASD-related genes and their distinctive signaling pathways and dysfunction relevant to a variety of autism spectrum-related phenotypes. In addition, systematic review on existing animal models of ASD is also provided. ASD has been linked to genes involved in synaptic transmission and scaffolding, chromatin remodeling, protein synthesis and degradation, and actin cytoskeletal dynamics, all of which are highly important for neuronal adaptations or synaptic strength or scaling (Bourgeron, 2015; Lee et al., 2017; Tatavarty et al., 2020). Thus, this review article provides insight into potential roles of adaptive mechanisms or synaptic plasticity in this multifactorial brain disorder.
In a separate research article, Lim, Kim et al. investigated potential interaction between lysophosphatidic acid (LPA) receptor-mediated pathway and dendritic deficits in a cell model of ASD. They have found that gintonin, a substance isolated from ginseng, has an effect on the dendritic growth of cultured striatal neurons. Gintonin is a lipoprotein composed of LPA and ginseng protein, and its effect is mediated via the LPA receptor. In their study, the loss-of-function of Slitrk5 or Shank3 genes-mediated reduction in dendritic complexity in primary striatal neurons was restored by gintonin treatment in vitro. Although further studies with an in vivo model should be complemented, this study implicates ASD-relevant deficits in neuronal development might be reversible or plastic in response to extracellular signaling molecules such as LPA.
Small, non-coding RNAs called microRNAs (miRNAs) inhibit the function of protein-coding transcripts, and thereby regulates various aspects of brain function including synaptic development and transmission as well as neuronal survival (Cho et al., 2019; Brennan et al., 2020). Bai et al. investigated the roles of miR-29a/b1 in aging and Parkinson's disease (PD). While miR-29a/b1 knockout mice display accelerated aging in the periphery, deletion of miR-29a/b1 alleviates MPTP-induced neuronal damages, glial activation and behavioral impairments. Interestingly, they observed an increase of miR-29a levels in the cerebrospinal fluid of PD patients compared to the levels in healthy subjects as well as in cultured microglia, glia and neurons treated with LPS or MPP+, a neurotoxin. It is intriguing to imagine that miR-29a might be initially elevated as a part of cellular compensatory mechanisms, but eventually aggravating disease progression. Further exploration of downstream targets and understanding the function of elevated miR-29a in specific cell types are warranted.
In summary, the four articles contributed by Bhatti et al., Lim, Yoon et al., Bai et al., Lim, Kim et al. in this Research Topic exemplify a great potential of studies of brain cells' compensatory mechanisms for identifying disease mechanisms, therapeutic targets or biomarkers. Because this Research Topic can be broadly applicable to a variety of biological systems, many new research avenues can be explored under the scope of this Research Topic in the future.
Author contributions
Both authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
Funding
YK was supported by the National Institutes of Health (R01MH121763), a Busch Biomedical Grant from the Office for Research at Rutgers University and a Seed Grant from the American Epilepsy Society. B-CS was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean Government Ministry of Sciences and ICT (Nos. 2022R1A2C100656011 and 2020R1A4A1019436).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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References
Ashraf, A., Fan, Z., Brooks, D. J., and Edison, P. (2015). Cortical hypermetabolism in MCI subjects: a compensatory mechanism? Eur. J. Nucl. Med. Mol. Imaging 42, 447–458. doi: 10.1007/s00259-014-2919-z
Bhat, S., Dao, D. T., Terrillion, C. E., Arad, M., Smith, R. J., Soldatov, N. M., et al. (2012). CACNA1C (Cav1.2) in the pathophysiology of psychiatric disease. Prog. Neurobiol. 99, 1–14. doi: 10.1016/j.pneurobio.2012.06.001
Blesa, J., Trigo-Damas, I., Dileone, M., Del Rey, N. L., Hernandez, L. F., and Obeso, J. A. (2017). Compensatory mechanisms in Parkinson's disease: Circuits adaptations and role in disease modification. Exp. Neurol. 298(Pt B), 148–161. doi: 10.1016/j.expneurol.2017.10.002
Bourgeron, T. (2015). From the genetic architecture to synaptic plasticity in autism spectrum disorder. Nat. Rev. Neurosci. 16, 551–563. doi: 10.1038/nrn3992
Brennan, G. P., Bauer, S., Engel, T., Jimenez-Mateos, E. M., Del Gallo, F., Hill, T. D. M., et al. (2020). Genome-wide microRNA profiling of plasma from three different animal models identifies biomarkers of temporal lobe epilepsy. Neurobiol. Dis. 144, 105048. doi: 10.1016/j.nbd.2020.105048
Chen, M. X., Oh, Y. S., and Kim, Y. (2022). S100A10 and its binding partners in depression and antidepressant actions. Front. Mol. Neuurosci. 15:953066. doi: 10.3389/fnmol.2022.953066
Cho, K. H. T., Xu, B., Blenkiron, C., and Fraser, M. (2019). Emerging Roles of miRNAs in Brain Development and Perinatal Brain Injury. Front. Physiol. 10, 227. doi: 10.3389/fphys.2019.00227
Cross-Disorder Group of the Psychiatric Genomics Consortium [Corporate Author] (2013). Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379. doi: 10.1016/S0140-6736(12)62129-1
Ferland, J. N., Hynes, T. J., Hounjet, C. D., Lindenbach, D., Vonder Haar, C., Adams, W. K., et al. (2019). Prior exposure to salient win-paired cues in a rat gambling task increases sensitivity to cocaine self-administration and suppresses dopamine efflux in nucleus accumbens: support for the reward deficiency hypothesis of addiction. J. Neurosci. 39, 1842–1854. doi: 10.1523/JNEUROSCI.3477-17.2018
Green, E. K., Grozeva, D., Jones, I., Jones, L., Kirov, G., Caesar, S., et al. (2010). The bipolar disorder risk allele at CACNA1C also confers risk of recurrent major depression and of schizophrenia. Mol. Psychiatry 15, 1016–1022. doi: 10.1038/mp.2009.49
Jin, J., Bhatti, D. L., Lee, K. W., Medrihan, L., Cheng, J., Wei, J., et al. (2020). Ahnak scaffolds p11/Anxa2 complex and L-type voltage-gated calcium channel and modulates depressive behavior. Mol. Psychiatry 25, 1035–1049. doi: 10.1038/s41380-019-0371-y
Koob, G. F., and Le Moal, M. (2001). Drug addiction, dysregulation of reward, and allostasis. Neuropsychopharmacology 24, 97–129. doi: 10.1016/S0893-133X(00)00195-0
Lee, E., Lee, J., and Kim, E. (2017). Excitation/Inhibition Imbalance in Animal Models of Autism Spectrum Disorders. Biol. Psychiatry 81, 838–847. doi: 10.1016/j.biopsych.2016.05.011
Liu, Y., Blackwood, D. H., Caesar, S., de Geus, E. J., Farmer, A., Ferreira, M. A., et al. (2011). Meta-analysis of genome-wide association data of bipolar disorder and major depressive disorder. Mol. Psychiatry 16, 2–4. doi: 10.1038/mp.2009.107
Lord, C., Brugha, T. S., Charman, T., Cusack, J., Dumas, G., Frazier, T., et al. (2020). Autism spectrum disorder. Nat. Rev. Dis. Primers 6, 5. doi: 10.1038/s41572-019-0138-4
Lord, C., Elsabbagh, M., Baird, G., and Veenstra-Vanderweele, J. (2018). Autism spectrum disorder. Lancet 392, 508–520. doi: 10.1016/S0140-6736(18)31129-2
McEwen, B. S., and Gianaros, P. J. (2011). Stress- and allostasis-induced brain plasticity. Annu. Rev. Med. 62, 431–445. doi: 10.1146/annurev-med-052209-100430
Ng, S. Y., and Lee, A. Y. W. (2019). Traumatic brain injuries: pathophysiology and potential therapeutic targets. Front. Cell. Neurosci. 13, 528. doi: 10.3389/fncel.2019.00528
Nudo, R. J. (2013). Recovery after brain injury: mechanisms and principles. Front. Hum. Neurosci. 7, 887. doi: 10.3389/fnhum.2013.00887
Pinggera, A., Lieb, A., Benedetti, B., Lampert, M., Monteleone, S., Liedl, K. R., et al. (2015). CACNA1D de novo mutations in autism spectrum disorders activate Cav1.3 L-type calcium channels. Biol. Psychiatry 77, 816–822. doi: 10.1016/j.biopsych.2014.11.020
Schizophrenia Working Group of the Psychiatric Genomics Consortium. (2014). Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427. doi: 10.1038/nature13595
Surgucheva, I., He, S., Rich, M. C., Sharma, R., Ninkina, N. N., Stahel, P. F., et al. (2014). Role of synucleins in traumatic brain injury - an experimental in vitro and in vivo study in mice. Mol. Cell. Neurosci. 63, 114–123. doi: 10.1016/j.mcn.2014.10.005
Svenningsson, P., Kim, Y., Warner-Schmidt, J., Oh, Y. S., and Greengard, P. (2013). p11 and its role in depression and therapeutic responses to antidepressants. Nat. Rev. Neurosci. 14, 673–680. doi: 10.1038/nrn3564
Tatavarty, V., Torrado Pacheco, A., Groves Kuhnle, C., Lin, H., Koundinya, P., Miska, N. J., et al. (2020). Autism-associated shank3 is essential for homeostatic compensation in rodent V1. Neuron 106, 769–777.e764. doi: 10.1016/j.neuron.2020.02.033
Vaupel, P., and Harrison, L. (2004). Tumor hypoxia: causative factors, compensatory mechanisms, and cellular response. Oncologist 9(Suppl. 5), 4–9. doi: 10.1634/theoncologist.9-90005-4
Keywords: brain cells, compensatory mechanism, homeostasis, risk factors, brain disorders
Citation: Kim Y and Suh B-C (2022) Editorial: Brain cells' compensatory mechanisms in response to disease risk factors. Front. Mol. Neurosci. 15:1096287. doi: 10.3389/fnmol.2022.1096287
Received: 11 November 2022; Accepted: 02 December 2022;
Published: 20 December 2022.
Edited and reviewed by: Andrei Surguchov, University of Kansas Medical Center, United States
Copyright © 2022 Kim and Suh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yong Kim, 
yk539@rwjms.rutgers.edu; Byung-Chang Suh, bcsuh@dgist.ac.kr