Schizophrenia (SZ) is a complex disorder with ~1% incidence world-wide involving multiple risk factors such as chemical imbalance (e.g., neurotransmitters), infection (e.g., Toxoplasmosis), genetic susceptibility (e.g., three-fold familial risk with a first-degree relative) and epigenetic factors (e.g., high discordance rates in monozygotic twins) (Saxena et al., 2021). Many genetic and animal model studies have been carried out to date, but we are far from a complete understanding of the molecular/physiological basis for the onset and progression of SZ (e.g., Winship et al., 2019; Trubetskoy et al., 2022). Considerable efforts were also directed for targeting molecules suspected to be involved in positive and negative symptoms to develop antipsychotic drugs [reviewed in Corell (2020)]. However, these efforts have not yet provided completely curable or long-term solutions, mainly because of the lack of comprehensive understanding of the basic mechanisms, processes involving relapse, heterogeneity in the molecular abnormalities in patients, etc. (e.g., Farsi and Sheng, 2023).
Among the different lines of research involved in studying the basic mechanisms, the present article collection focuses on molecular pathways involved in SZ. In general, diverse sets of molecular pathways, broadly belong to four main categories of cellular signaling that are essential for various neurodevelopmental processes such as neuron cell survival, growth, death, neuron-to-neuron signaling, etc. (Table 1). Many of these pathways have been used to study the effects of some commonly used antipsychotic drugs, development of new generation of drugs and understand the basis of non-responsiveness in some cases. Nevertheless, as mentioned above, both basic and applied research are needed for better diagnosis and management of SZ.
Table 1
| Category | Molecules | Interacting molecule | Post-synaptic potential or biological effect | Effect of antipsychotic drugs | References |
|---|---|---|---|---|---|
| Neuron—neuron signaling | GABA Glutamate Acetylcholine Dopamine Serotonin Epinephrine Oxytocin |
GABA Receptor mGlu Receptors Ach Receptor D1/D2 Receptors Serotonin Receptor β1/β2/β3 Receptors Oxytocin R |
Inhibitory Excitatory or inhibitory Excitatory Excitatory Inhibitory Excitatory or inhibitory Excitatory |
No effect Decrease – Decrease Decrease Increase – |
Yoon et al., 2020 Merritt et al., 2021 – Kehr et al., 2018 Kehr et al., 2018 Boyda et al., 2020 – |
| G protein—coupled receptors | Dopaminergic receptor | Gαq | Excitatory | Inhibition | Servonnet and Samaha, 2020 |
| Serotonergic receptors 5-HT1Rs 5-HT2Rs 5-HT4Rs 5-HT5A |
• Gαi/o Gαq Gαs Gαi/o |
• Inhibitory Excitatory Excitatory Inhibitory |
• Activation Both Both No data |
Ochiai et al., 2022 Giovanni and Deurwaerdère, 2016 Agrawal et al., 2020 – |
|
| Glutamatergic receptors mGluR1 mGluR5 mGluR2, mGluR3 mGlu4, mGlu6, mGlu7, & mGlu8 |
• Gαs and Gαq Gαs and Gαq Gαi/o Gαi/o |
• Excitatory Excitatory Inhibitory Inhibitory |
• Activation Inhibition Inhibition – |
Korlatowicz et al., 2021 Korlatowicz et al., 2021 Revenga et al., 2019 – |
|
| Receptor tyrosine kinases | BDNF EGF FGF WNT |
BDNF receptor EGF receptor FGF receptor Frizzled |
Neuron survival/regeneration Neuron survival/differentiation Neuron survival Neuron survival/differentiation |
Normal levels No effect Increase Pathway Activation |
Noto et al., 2021 Zhang et al., 2020 Li et al., 2022 George et al., 2020 |
| Intracellular receptors | Retinoic acid receptor Estrogen Testosterone Cortisol Thyroid hormone Vitamin D |
Vitamin A Estrogen receptor Androgen receptor Glucocorticoid receptor T3 Receptor Vitamin D receptor |
Neuron differentiation Neuroprotection Neuroprotection Neuronal death Neuroprotection Neurogenesis |
Increase/stabilization Decrease Increase Decrease Decrease No change |
Regen et al., 2021 Piriu et al., 2015 Huang et al., 2021 Tobolska et al., 2016 Zhang and Lin, 2020 Kopecek et al., 2019 |
Selected pathways among the four broad categories of signaling processes and their relevance to schizophrenia.
Among the collection of articles under the Research Topic, one investigation involved the programmed cell death—associated genes dysregulated in SZ patients (Feng and Shen) who used transcriptome data from dorsolateral prefrontal cortex from the publicly available database containing 58 SZ patients and 175 controls as discovery group. The choice of cell death—related genes was also important because of the observations that the SZ patients showed accelerated aging effects with loss of gray and white matter (Cropley et al., 2017). Out of the 2,684 differentially expressed genes (DEGs) identified, 263 were among the genes linked to programmed cell death. Following extensive bioinformatic analysis including machine learning, protein-protein interactions and consensus cluster analysis, the authors identified 10 most differentially expressed genes (DPF2, ATG7, GSK3A, TFDP2, ACVR1, CX3CR1, AP4M1, DEPDC5, NRFA2, and IKBKB) that are also involved in different forms of cell death. The diagnostic value of expression states of these genes, when assessed by ROC curve analysis yielded an AUC of 0.91. These results were further confirmed using a validation dataset from BA10 (anterior prefrontal cortex) areas of 19 controls and 23 patients (AUC: 0.94). Further, when the proportions of immune cells were estimated using the ImmuneCellAI algorithm, the affected tissues showed significant differences in the levels of cytotoxic and natural killer cells. Finally, gene-drug interaction analysis identified aflatoxin B1, valproic acid (VPA), arsenic, benzo(a)pyrine, epigallocatechin gallate (EG) and nickel as interacting drugs. Together, the data from Feng and Shen suggest that: (1) At least a subset of patients can be diagnosed based on dysregulated states of the identified set of the 10 cell death—related genes and (2) Drugs such as VPA and EG may be useful for treatment of this subset. Of these, VPA is known to increase the levels of GABA, block voltage-gated ion channels and inhibit histone deacetylase (HDAC) activity (Ghodke-Puranik et al., 2013).
The second article in this Research Topic focused on perturbation of the levels of DNA methyltransferase 1, required for maintenance of DNA methylation an important epigenetic modification (Mohan and Chaillet, 2013). Singh et al. based their study on the observations that DNMT1 overexpression is a risk factor for SZ, epilepsy and bipolar disorders (Veldic et al., 2005; Zhu et al., 2012) and used genetically modified mouse embryonic stem cell line that overexpresses the enzyme. Interestingly, the same cell line can be made to turn off the Dnmt1 expression by treatment with doxycycline. Transcriptome analysis of the neurons produced by these cells under both doxycycline-treated and untreated conditions identified ~3,000 dysregulated genes for each category. Several of these genes were involved in neurodevelopmental processes, neurotransmission, synaptic function, extracellular signaling, cell–cell junctions, extracellular matrix interactions, DNA replication, DNA repair, translation machinery, etc. These genes were also subjected to transcript level changes in patients with any of the three disorders as well as autism spectrum disorder. This data provided evidence in support of the hypothesis that both loss as well as increased expression of DNMT1 as factors influencing abnormal behavior and that DNMT1 levels need to be maintained within a normal range for better outcomes (Mohan, 2022).
Both studies under this Research Topic point to important common factors that are involved in epigenetic modifications of mammalian genomes. It is well established that DNA methylation at promoters influences gene expression and the effects involve cooperative action of DNMT1 and HDAC1/HDAC2 in establishing and maintaining repressive histone modifications at their N-terminal tails (e.g., H3-K9 Me3) at methylated promoters (Burgers et al., 2002). The studies by Feng and Shen, and Singh et al. further implicate the involvement of epigenetic machinery (HDAC1 and DNMT1, respectively). It is noteworthy that a subset of SZ patients shows increased HDAC1 levels (Sharma et al., 2008), but it is not known whether these patients also have increased DNMT1 levels. In this context, investigations are needed to test DNMT1 overexpression effects on HDAC1 levels. Nevertheless, drug-based modulation of DNMT1 and HDAC1 levels/activity to normal ranges holds promise for better treatment of a subset of patients with SZ and possibly with other mental health disorders wherein either or both genes show dysregulation.
Statements
Author contributions
KM: Writing – original draft, Writing – review & editing.
Funding
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Research in the author's laboratory was supported by grants from Scientific and Engineering Board, Department of Science and Technology, Department of Biotechnology and Indian Council of Medical Research.
Acknowledgments
Funds under the Centre for Human Disease Research initiative and OPERA scheme from BITS Pilani are thankfully acknowledged. The author thanks Anuhya Anne, Minali Singh, and Sumana Choudhury, for their contributions and their involvement in discussions at various stages.
Conflict of interest
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
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Summary
Keywords
schizophrenia, DNMT1, programmed cell death, valproic acid, HDAC1, HDAC2, antipsychotic drug, DNMT1 inhibition
Citation
Mohan KN (2024) Editorial: New insights into investigating schizophrenia as a disorder of molecular pathways. Front. Mol. Neurosci. 17:1360616. doi: 10.3389/fnmol.2024.1360616
Received
23 December 2023
Accepted
02 January 2024
Published
10 January 2024
Volume
17 - 2024
Edited and reviewed by
Jean-Marc Taymans, Institut National de la Santé et de la Recherche Médicale (INSERM), France
Updates
Copyright
© 2024 Mohan.
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*Correspondence: Kommu Naga Mohan kommumohan@gmail.com
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.