Role of Endogenous Metabolite Alterations in Neuropsychiatric Disease

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The role of endogenous metabolite alterations in neuropsychiatric disease gregg crabtree, and Joseph Gogos ACS Chem. Neurosci., Just Accepted Manuscript • DOI: 10.1021/acschemneuro.8b00145 • Publication Date (Web): 25 Jul 2018 Downloaded from http://pubs.acs.org on July 26, 2018

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ACS Chemical Neuroscience

The role of endogenous metabolite alterations in neuropsychiatric disease

Gregg W Crabtree1,2 and Joseph A Gogos1,2,3

1

Department of Physiology and Cellular Biophysics, Columbia University Medical Center, New York, NY 10032;

2

Zuckerman Mind Brain Behavior Institute, New York, NY 10025;

3

Department of Neuroscience, Columbia University Medical Center, New York, NY 10032.

Correspondence: Joseph A Gogos ([email protected]) or Gregg W Crabtree([email protected])

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ABSTRACT The potential role in neuropsychiatric disease risk arising from alterations and derangements of endogenous small molecule metabolites remains understudied. Alterations of endogenous metabolite concentrations can arise in multiple ways. Marked derangements of single endogenous small-molecule metabolites are found in a large group of rare genetic human diseases referred to as “inborn errors of metabolism,” many of which are associated with prominent neuropsychiatric symptomology. Whether such metabolites act neuroactively to directly lead to distinct neural dysfunction has been frequently hypothesized but rarely demonstrated unequivocally. Here we discuss this disease concept in the context of our recent findings demonstrating that neural dysfunction arising from accumulation of the schizophrenia-associated metabolite L-proline is due to its structural mimicry of the neurotransmitter GABA that leads to alterations in GABA-ergic short-term synaptic plasticity. For cases in which a similar direct action upon neurotransmitter binding sites is suspected, we lay out a systematic approach that can be extended to assessing the potential disruptive action of such candidate disease metabolites. To address the potentially important and broader role in neuropsychiatric disease, we also consider whether the more subtle yet more ubiquitous variations in endogenous metabolites arising from natural allelic variation may likewise contribute to disease risk but in a more complex and nuanced manner.

Key Words: Psychiatric Disease, Neurological Disease, 22q11.2 deletion syndrome, Metabolites, Metabolomics, Neurotransmitters, synaptic plasticity, Inborn Errors of Metabolism (IEM).


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Introduction The pathogenesis and pathophysiology of neuropsychiatric diseases involves a complex interplay genetic and environmental factors1-4. Our current understanding of these diseases suggests that both developmental alterations in the wiring of brain circuitry5 as well as ongoing alterations in the synaptic activity and plasticity6, 7 of neural circuits in the adult brain may often play roles in leading to disease. The latter pattern of dysfunction is supported by observations of (1) periods of apparently normal, healthy function as well as frequently excellent clinical responses to medications in patients with disease and (2) the ability of many medications and drugs to acutely precipitate symptoms – for example seizures8 or acute psychosis9 – in otherwise healthy and disease-free individuals that are nearly indistinguishable from those found in broader neuropsychiatric disease. Here we consider the important yet understudied potential disease roles of alterations in the levels of endogenous small molecules in contributing to ongoing deficits in activity and plasticity of neural circuits underlying neuropsychiatric dysfunction6, 10-13. Normally the levels of the enormous array of unique small molecule metabolites14 are usually kept tightly regulated by the activity of a very large array of enzymes and transporters responsible for the production, transformation, degradation, and compartmentalization of these small molecules15. In humans, however, there are a large number of diverse and individually relatively rare genetic diseases in which the metabolism of discrete endogenous small molecule metabolites is dysregulated leading to elevated levels of specific metabolites in both peripheral tissues as well as the brain16. These diverse diseases arise, for example, when a gene encoding a critical enzyme involved in degrading a given metabolite suffers a mutation that causes substantial loss of enzyme activity resulting in levels of its substrate metabolite rising far beyond its normally regulated physiological levels17. Importantly, a substantial subset of these diseases frequently display prominent neuropsychiatric dysfunction18, 19. Whether the deranged metabolites may be directly contributing to disease and precisely how they cause dysfunction, however, has rarely been convincingly demonstrated. Recently we have described such a disease mechanism for the suspected human neuropsychiatric disease metabolite L-proline and its degradative enzyme proline dehydrogenase (PRODH)7. L-proline elevations arise from diminished function of PRODH in the human diseases hyperprolinemia type I (HPI) and 22q11.2 deletion syndrome (22q11DS)17, 20, which together show increased risk for psychotic disorders, schizophrenia and seizures21, 22. Using a systematic bottom-up approach toward disease mechanism we found that L-proline acted neuroactively through structural mimicry of the neurotransmitter GABA leading to its direct interaction with GABA binding sites. This led to disruption of GABA-ergic short-term synaptic plasticity that resulted in marked dysfunction in the organization of synchronized activity of neuronal ensembles7, 23 . Here, in the context of the conceptual framework of our approach and discoveries with L-proline/PRODH dysregulation employed as paradigm, we consider a number of core issues critical to elucidating and establishing the broader role of metabolite dysregulation in neuropsychiatric disease: i) How specific metabolite derangements could cause or contribute to disease dysfunction; ii) How to systematically assess a candidate dysregulated metabolite suspected of similar direct disruptive neuroactivity at neurotransmitter signaling targets though targeted, tractable assays; iii) How to assess the consequences of these findings upon neuronal, synaptic, in vivo network and behavioral function. We discuss recent evidence supporting that structural mimicry of GABA may play a common role in several other diseases that show overlapping neuropsychiatric clinical



phenotypes7 and that have elevations in structurally similar metabolites. Finally, we highlight critical issues surrounding the potential of a much broader and ubiquitous role of neuroactive metabolite variation in neuropsychiatric disease10, 13, 18, 19. Inherent lability of brain states to relatively minor changes in the basal operating conditions. To understand the potential role that specific metabolite derangements might play in causing or contributing to neuropsychiatric dysfunction it is informative to reflect upon how the exceptionally tight regulation of other more general factors contributes to both normal and abnormal functional brain states. The brain and its underlying, interconnected neural networks represents a complex system with functional states that may be either stable and operationally robust or operationally vulnerable under any given set of working conditions24-26. In the human brain the chemical reactions and interactions that regulate cellular function to permit normal neuronal communication take place under scrupulously controlled conditions. Conditions including temperature, pH, ion concentrations, and osmolarity are all tightly regulated and rarely deviate by more than 1-2% from their normal values within a given individual under normal circumstances27-29. Even minor fluctuations in any of these variables can lead to dramatically dysfunctional brain states. Fever, ionic imbalances, or acidosis can each acutely lead to marked changes in mental status including precipitating either psychosis30-33 or seizures32, 34 in patients with otherwise presumptively normal brain function and neuronal circuitry. That even minor perturbations in conditions away from their normal, resting levels can lead to dramatic aberrations in the functional state of brain neural networks highlights the functional significance of exceptionally tight regulation of brain environmental conditions. These considerations suggest that “normal” healthy functional states of the human brain may be rather precarious and inherently unstable states that are only maintained due the constant vigilance of a multitude of homeostatic mechanisms that stabilize operating conditions within this complex system of interconnected networks35. Similar to the factors mentioned above, the levels of the very large array of endogenous small molecule metabolites in the brain are also normally tightly regulated variables suggesting a functional need for such regulation15, 36. By reasonable inference, the foregoing clinical data suggest the possibility that if the concentrations of small molecule metabolites within the CNS deviated significantly from normal resting levels then such a perturbation could likewise lead to substantial dysfunction and contribute to neuropsychiatric symptomology. Endogenous small molecule metabolites as potential contributors to neuropsychiatric disease While many factors contribute to fine sculpting information processing in the human brain, neuron-to-neuron communication is largely dictated by the signals mediated by small molecule neurotransmitters (NTs)37. To understand how a neuroactive metabolite might alter the signaling of a native neurotransmitter, it is necessary to consider its possible actions at any of the components within a given NT system that metabolize, transport, or detect native neurotransmitters. The levels of the endogenous small molecules present in the brain are normally tightly regulated15, 36. Due to the under-explored nature of the human metabolome, however, comprehensive data regarding the normal levels of endogenous small molecules and their possible native signaling roles in the human brain remains incomplete and fragmentary14, 38-40. The potential role that derangements in these small


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molecule metabolites might play in neuronal dysfunction in neuropsychiatric disease has long been postulated18, 19 but has remained largely speculative and definitive mechanisms leading to neuronal dysfunction are poorly defined in the literature (TABLE 1). Derangements of the levels of any of the enormous array of small molecule metabolites within the brain could lead to neuronal dysfunction if these metabolites are capable of acting neuroactively at targets normally involved in neuronal signaling. The term “neuroactive” is a broad and vague descriptor that implies capable of acting at any of the discrete protein targets within the nervous system involved in neuronal signaling so as to modify neuronal signaling or communication. Here, for concision of treatment, we will substantially narrow this definition by defining “neuroactive” as describing a small molecule capable of interacting with any of the discrete proteins that normally synthesize/create, catabolize/degrade, translocate, or detect and signal through interaction with specific endogenous small-molecule neurotransmitters55. In theory a candidate neuroactive disease metabolite could modify the function of any of these NT-related targets either (1) by acting competitively (due to significant structural homology to a native NT) or (2) by acting non-competitively (allosterically) at sites on these targets not directly related to NT binding56 or (3) by acting indirectly to modify the function of NT systems – for example, by acting at kinases or phosphatases that normally act to modify the function of NT-system components57. For potential competitive actions of metabolites at NT-binding sites, the chemical structure of the metabolite can be used to predict likely NT-system interactions based upon structural homology with native neurotransmitter structures. In contrast, in cases of possible non-competitive/allosteric actions or indirect actions of metabolites – with the exception of previously characterized binding sites56 – there is no systematic means to use metabolite structures to predict target interactions as the molecules that bind such target sites are almost always unrelated to NT structures. Due to these complexities, here we limit our consideration to that subset of metabolites possibly acting directly at NT binding sites due to their structural homology to the native NTs where the logic of predicting possible metabolite-target interactions is much more straightforward. For similar reasons, we consider only “small-molecule” NTs, excluding neuropeptide NTs which have complex chemical structures58 where, again, predictions of potential metabolite interactions with targets based upon structural homology are more difficult. In the following section we consider (1) the spectrum of a NT-system’s components, (2) the possible interfering actions and consequences of neuroactive metabolites acting at these targets, and (3) outline a systematic approach to the comprehensive evaluation of a candidate neuroactive disease-associated metabolite suspected of acting directly at targets with NT binding sites. To this end, we employ as example our recent approaches with L-proline7 that led to findings of a direct competitive antagonistic action at the GABA binding site of the GABA synthesizing enzyme glutamate decarboxylase (GAD) that was responsible for circumscribed GABA-ergic transmission plasticity deficits. As the GABA-ergic system targets and functions are prototypical of all known neurotransmitters systems, these principles and approaches can be readily extended to assess any candidate metabolite’s suspected action upon the distinct NT targets of any neurotransmitter system suspected in disease dysfunction55, 59. Determining NT-system targets of a disease-associated metabolite The neural mechanisms underlying the neuropsychiatric symptomology often prominent in some human inborn errors of metabolism (IEMs) largely remain poorly understood (18,19). The circumscribed molecular defects underlying these diseases strongly suggest the possibility that the resulting elevations of distinct metabolites in the



CNS may be the direct, proximate cause of neural dysfunction due to interfering actions upon neural signaling pathways. Despite the well-defined molecular nature of these diseases, efforts to determine the precise neuroactive targets and molecular mechanisms responsible for neural dysfunction have largely been non-systematic and have only rarely described convincing disease mechanisms. To partially address this issue, here we lay out a bottom-up, systematic approach to effectively determine the disease-relevant targets of rare disease metabolites suspected of acting at targets with neurotransmitter binding sites. Employing an etiological valid animal model of disease To effectively determine the detailed molecular mechanisms involved in human disease requires a level of invasiveness only permitted by animal models. In the case of modeling human neuropsychiatric IEMs, an etiologically valid animal model of human disease should recapitulate (1) the human genetic error, (2) the magnitude of metabolite derangement and (3) demonstrate endophenotypes and/or behavioral correlates reflective of human disease symptoms. As example, in the case of human hyperprolinemia type 1 extensive studies have determined the spectrum of PRODH disease mutations and established their impacts upon L-proline level elevation17, 20, 21. This wealth of well-defined genetic and functional data permitted the creation of transgenic mouse models that faithfully replicate the key alterations found in human disease44, 61. Specifically, for our studies we employed a mouse with a hypomorphic allele (approximating Prodh-null) that recapitulated the diminished PRODH activity, elevated L-proline levels, and multiple behavioral alterations consistent with those found in human disease44, 61. Assessment of metabolite chemical structure Structural comparisons and database searches can be used to reveal whether disease-associated metabolites bear significant homology to specific neurotransmitters (NTs), other neuroactive small molecules, or other disease-associated metabolites dysregulated in IEMs with similar neuropsychiatric manifestations. Findings of structural homology that point to possible metabolite actions at specific molecular targets are then used to guide interpretation of electrophysiology findings and to direct testing of metabolite actions at these suspected targets of action. As example, in considering potential targets of L-proline neuroactivity in disease, both glutamatergic and GABA-ergic systems were of significant consideration. Metabolically, L-proline resides only two and three enzymatic steps away from Lglutamate and GABA, respectively, the only two NTs in the brain to which it has significant structural similarity (Figure 1). The structure of L-proline, however, would appear a much better match for GABA due to their shared absence of a second carboxylate group present in glutamate. Additionally, multiple reports have demonstrated L-proline action at GABA-transporters and GABA action at proline transporters indicating biologically relevant structural homology62-64. Further, in human diseases with elevated CNS L-proline, the combined clinical associations with schizophrenia, psychotic disorders, and seizures17, 20-22, 60,61 pointed to a possible common disease mechanism involving GABA-ergic dysfunction5 (Table 2, Table 3). Structural database searches also revealed that at least three additional disease-associated metabolites linked to seizures and psychosis bore marked structural similarity to L-proline and GABA41-43, suggesting the possibility of a broader and shared molecular disease mechanism involving GABAergic dysfunction. (Figure 2, Table 2). Unbiased electrophysiological assessment in a disease-relevant brain region


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Both the spectrum of neuropsychiatric manifestations and metabolite structural considerations may together point towards suspicion of metabolite-induced dysfunction within a particular NT system. Due to the complex regulation of neural circuit function by NT-systems, however, it is difficult to predict the details of possible dysfunctional outcomes. It is therefore critical that initial electrophysiological assessment in a diseaserelevant brain region6 should be broad, unbiased, and “agnostic” as to possible neuronal and synaptic dysfunction. As example, although preliminary considerations in the case of L-proline strongly suggested possible GABA-ergic dysfunction, we nonetheless performed a comprehensive electrophysiological analysis that included both excitatory and inhibitory synaptic transmission and plasticity in hyperprolinemic mice. Additionally, we performed this assessment in the prefrontal cortex (PFC) as this region has been strongly implicated in the dysfunction underlying psychosis and schizophrenia 5,6. These studies revealed that almost all forms of synaptic transmission and plasticity were unaltered. Specifically, both basic glutamatergic transmission and all forms short-term synaptic plasticity tested were unaltered. For GABA-ergic transmission, basic transmission and very short-term plasticity (paired-pulse ratio, PPR) were similarly unaltered. Our studies detected only a single, very specific functional alteration in synaptic function: deficits in sustained high-frequency (HF) GABA-ergic transmission6, 7 (Figure 3). In the context of the observed structural homology of L-proline and GABA, these isolated changes in short-term GABA-ergic plasticity specifically suggested likely actions of L-proline at GABA-ergic targets to be underlying this dysfunction. Determining disease relevant CNS metabolite concentrations In cases where disease-metabolite structure and detected electrophysiological dysfunction together point to likely dysfunction of a specific NT-system, functional assays of metabolite action at the discrete molecular components of that NT system can be employed to detect interference with their normal function. Prior to testing metabolite actions at the NT binding sites of these targets, however, both the normal and diseaserelevant metabolite concentrations likely to be found near these sites must be determined. As NT systems have components with NT binding sites in both extracellular and intracellular compartments, it is critical to have reliable estimates of metabolite levels for both compartments to ensure relevant concentration testing. In general, reliable data for metabolite levels in these neuronal compartments is frequently absent or sparse17-21, 38, 39. Presently most studies have determined disease metabolite levels based on peripheral blood and (much less frequently) cerebrospinal fluid (CSF) samples both of which unfortunately often serve as poor proxies for metabolite levels in the direct vicinity of neurons38-40. When reliable values of perineuronal metabolite levels are not available, it is a reasonable alternative to employ the animal model of the human disease to determine estimates for CNS (1) extracellular levels (via in vivo brain microdialysis) and (2) intracellular levels (derived from brain homogenate measures, which are strongly weighted volumetrically towards intracellular space). Assessing potential metabolite actions at NT-system components When considering possible sources of neural dysfunction arising due to metabolite interactions with NT binding sites, the full repertoire of targets with such sites should be taken into account. In addition to extracellular NT receptors, both the transporters that move NTs across membranes and the enzymes that produce and degrade NTs possess NT binding sites that could be disease-relevant sites of metabolite action. That distinct clinical pharmaceuticals can significantly modify neural function by



acting at each of these broad classes of NT targets (Table 3) indicates they all represent credible and potentially relevant targets of metabolite-induced dysfunction. These facts highlight the need for broad functional screening across all of these targets for possible metabolite actions leading to altered function. Functional assays of NT-system components will reveal whether metabolites interacting with NT binding sites act to modify target function by acting as agonists, antagonists, or as competitive substrates (Table 3). While the details of possible outcomes on neuronal and synaptic function may be difficult to predict, findings of these functional assays here should point to a limited set of possible or probable outcomes that can then be directly tested with targeted electrophysiology assays of neural function (see following section, below). As example, to determine the specific GABA-ergic target(s) of L-proline responsible for the observed altered GABA-ergic plasticity, we assessed potential actions of L-proline at GABA binding sites by undertaking a comprehensive assessment of its effects on the function of the major GABA-system components (Table 3, Figure 4). Using both normal and disease-relevant levels of L-proline (Figure 4), we interrogated the action of L-proline across these GABA-system receptors, transporters, and enzymes by employing tractable, well-established in vitro functional assays55 (Table 3). While we observed L-proline actions at the GABA binding sites of multiple GABA-system targets (Table 3), most of these effects did not occur at disease-relevant concentrations or could not be easily reconciled with the detected GABA-ergic transmission alterations7. These findings largely ruled out significant roles of most GABA-system components in contributing to disease dysfunction. Targeted re-probing of neural function to determine disease mechanism In cases where disease-relevant concentrations of metabolites are found to act at discrete NT-system components, these findings serve to direct more targeted and refined electrophysiological assessment in the animal model. Specifically, such findings inform directed assays designed to detect alterations of neural function predicted to likely arise due to disrupted function of the suspected NT-system component (Table 3). Results of these studies serve to determine whether detected alterations in neural function are consistent with altered function of the suspected NT-system component and thus serve to potentially support proposed disease mechanisms. Additional mechanistic support can be achieved by employing pharmacological or transgenic strategies targeted to recapitulate suspected alterations in NT-component function that should permit mimicking disease-associated neural dysfunction. Conversely, reversal (or partial reversal) of this dysfunction in the animal model should be attempted by targeted pharmacological or transgenic strategies designed to overcome the suspected alteration in NT-component function. In our functional screen of potential L-proline actions across GABA-ergic system components, for example, we found that only a small subset of such actions occurred at disease-relevant L-proline concentrations, thus limiting the number of potential diseaserelevant mechanisms. (Table 3). Additionally, only our finding of L-proline competitive blockade of the GABA binding site of GAD that led to GABA production deficits was consistent with our finding of deficits in sustained HF GABA-ergic transmission67(Table 3). Re-probing synaptic physiology, we further found (1) that pharmacological blockade of GAD in WT mice recapitulated disease-associated transmission deficits and (2) that pharmacological interventions that blocked GABA degradation and increased net GABA production rescued Prodh-null GABA-ergic transmission deficits7. Together these results supported that L-proline blockade of GAD was likely the responsible mechanism underlying observed HF GABA-ergic transmission deficits.


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Testing disease-relevant higher order neural function Finally, to elucidate the disease-relevant impact of detected neuronal and synaptic dysfunction, assays of neural circuit function should be considered to reveal sources of altered information processing relevant to disease. As different neuropsychiatric diseases have recently been more precisely associated with distinct patterns and profiles of neural circuit and neural connectivity alterations (6), assays can often be meaningfully targeted to focus upon disease-specific neural circuit substrates. In the case of schizophrenia, for example, the broadly reported alterations in parvalbumin positive (PV+) GABA-ergic interneurons (and related gamma oscillations) as well as inter-regional dysconnectivity point towards assays of disease-relevant neural circuit function (6). Finally, behavioral assessment employing pharmacological strategies found to reverse electrophysiological alterations should be considered to determine whether they can also effectively ameliorate behavioral alterations. With these considerations in mind, in our assessment of neural circuit dysfunction arising from L-proline dysregulation, we employed in vivo field recordings in the mPFC and found network synchronization deficits specifically limited to gamma-band oscillations, similar to those alterations found in schizophrenia85 of these cognitively important oscillations that rely heavily on HF GABA-ergic transmission. Specifically, gamma oscillations are driven by high frequency GABA-ergic transmission arising from parvalbumin positive (PV+) GABA-ergic neurons69, and deficits of these neurons and oscillations have both been strongly linked to schizophrenia80, 85. Further, high frequency synaptic transmission arising from PV+ neurons is known to be critically involved in both fear learning86 and prepulse inhibition (PPI) of the startle reflex78, the same limited set of behaviors found altered in Prodh-null mice44, 61 and that represent endophenotypes of the cognitive and sensory-motor gating deficits in schizophrenia87, 88. In further support of our interpretation that GAD blockade likely leads to these specific dysfunctions, the Gad65-null mouse similarly demonstrates alterations largely limited to deficits in HF GABA-ergic transmission, fear learning, and startle PPI67, 77, 79. Taken together, our findings with L-proline7 highlight that in cases when a metabolite substantially mimics the structure of a specific NT it can directly act neuroactively at the NT binding sites of precise NT targets leading to very specific, predictable, and disease-relevant alterations in synaptic6, network85, and behavioral function87, 88. In summary, by employing a systematic, bottom-up approach analogous to our approach with L-proline – and as more generally outlined step-wise in Figure 4 – it should be possible to reveal the disease-relevant targets of nearly any diseaseassociated metabolite that acts principally at NT binding sites due to structural homology with native NTs. This approach begins with unbiased electrophysiology assessment in a valid animal model of disease combined with chemical structural assessment of the disease metabolite to (likely) principally attribute detected alterations to a particular NT system 7, 61. Next, disease-relevant extracellular and intracellular metabolite concentrations are used to comprehensively probe metabolite actions at all relevant targets within the suspected NT-system with tractable in vitro functional assays17, 61, 89 (Table 3). Finally, results from NT-system studies guide electrophysiology and behavioral studies in the animal model employing NT-system-targeted interventions to determine the relevance of in vitro findings in contributing to neuronal and synaptic dysfunction7 (Figure 4). Our studies of L-proline revealed that L-proline’s direct action at the GABA binding sites of most – but not all – GABA-ergic targets was competitively “antagonistic” in nature and would likely predominantly lead to diminished GABA-ergic function (Table 3). That most GABA-targeted clinical therapies work toward enhancing GABA-ergic



function further suggests that GABA-ergic hypofuction might be a common endpoint in disease68. Analogous to the case of GABA, almost all other NT-system directed therapies for disease similarly act to enhance NT-system signaling90 (with the notable exception of the agents suppressing dopaminergic function that dominate schizophrenia therapies91).Taken together, these considerations support that hypofuction of NTsystems may be a common outcome in many diseases and suggests that diseaseassociated metabolites may in many cases act to cause similar hypofuction7. However, although hypofunctional NT signaling may be a common outcome, depending on a given metabolite’s specific NT system target(s) and locations of action the resulting functional and behavioral outcomes should be anticipated to be both specific and circumscribed. The broader disease role of endogenous metabolites Both the foregoing discussions and our recent study have considered the disease role of the isolated, marked elevation of a single neuroactive metabolite found characteristically in “rare” diseases16, 18, 19. The complex, multifactorial nature of human disease risk, however, leads us to strongly suspect that variations in endogenous metabolite levels could be playing a subtler yet much broader contributory role across neuropsychiatric diseases1-4. There are multiple scenarios – alterations in blood-brain barrier permeability48, variation between gut microbiomes50, 52-54, and normal allelic variation in enzymes and transporters20, 21, 45 (Table 1) – in which more subtle yet broader alterations in metabolites could speculatively influence disease risk. Among these possibilities, it is likely that the most ubiquitous and significant contributor to broad disease risk will result from the complex effects arising out of allelic variations45, 47. Indeed recent genetic and epidemiological data suggest that nearly all human common and complex diseases are predominantly polygenic and multifactorial in nature, with substantial risk arising due to naturally inherited or de novo allelic variation 1, 47 . This suggests the possibility of a similarly complex interplay between variations in metabolite levels and activity arising from normal allelic variation and allelic interaction in contributing to disease risk (Table 1). Evidence supporting the disease-relevance of this notion can even be found in the case of L-proline where there appear to be important disease-risk implications arising due to possible metabolite-allele and/or metabolitemetabolite interactions44, 45. To reveal the disease risks arising from variations in the complex interactions within individual metabolomes requires different approaches and different tools than the targeted metabolomic methods used to assess disease risk due to individual metabolites on a case-by-case basis92. Such studies will require comprehensive, unbiased, untargeted metabolomic approaches93 to detect the full spectrum of (often subtle) variations between individual metabolomes in order to reveal the disease-predisposing synergies present among metabolites20, 45. Eventually, disease-risk assessment will require computational methods to detect possible synergies among metabolites within metabolic and functional networks and will likely additionally require computational methods combining metabolomic and genomic analysis46, 94. In this regard, most current analytical approaches appear inadequate to this task as they focus assessments based on our current understanding of the roles of metabolites rather than in detecting novel and unknown actions and functional relationships between metabolites 93. However, brain metabolomics is still in its “infancy” and of the vast number of unique molecules in the human metabolome we still know very little about the details of the CNS roles (including potential neuroactivity) of most of these endogenous small molecules14, 40. As such, there are currently many profound gaps in our knowledge of the human brain small-molecule metabolome preventing us from approaching and understanding its


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roles in both health and neuropsychiatric disease. Below we highlight two such critical gaps. Efforts to address these gaps are necessary and fundamental first steps towards eventually unraveling the mechanisms underlying disease risk arising from common, natural variations in metabolites. 1. We have little detailed knowledge of the “normal” in vivo human brain metabolome. We currently have a very incomplete understanding of the human brain small molecule metabolome. Of the enormous array of known endogenous small molecules in the human metabolome, the normal CNS levels and the extent of potential functional roles of only a very small fraction of these molecules has been examined14, 40, 95. Even “best-efforts” are currently only capable of resolving and quantifying a few hundred distinct metabolites 40, 96. Thus, sustained effort is needed to develop strategies employing comprehensive, unbiased, untargeted high-resolution mass-spectrometry (MS) of the CNS metabolome97 and much work remains to move toward an expanded definition of the levels of the “full” array of CNS small molecule species. Employing current animal model-based technologies, there still remains significant room to expand our knowledge of in vivo CNS metabolomic profiles including whole brain (intracellularly-weighted)96, ECF (relevant extracellular)98, and CSF36, 99. Despite this fact only a small number of groups have been pioneering such studies with our knowledge remaining very incomplete36, 96, 100.To move beyond the current limitations of molecular breadth and resolution will require development of new strategies or creative adaptations of existing technologies92, 97, 99, 101-103. Regarding human studies, due to the significant ethical considerations surrounding invasive procedures, at present such studies have only been broadly feasible for human CSF40. Additional studies to date have been very limited and confined to post-mortem tissue analysis of “whole brain” levels95. To partially overcome these challenges, additional development of non-invasive and less invasive techniques for determining in vivo metabolite levels in humans need further pursuit104. For example, there is much work to be done in the development of expanded and enhanced NMR/fMRI techniques for in vivo imaging to broaden the repertoire of metabolites that can be detected and reliably quantified in vivo 105-107. 2. We lack detailed knowledge about the quantitative compartmentalization of the CNS metabolome. Currently most data regarding metabolite levels in the human CNS have been restricted to levels determined in CSF (human, animal models)40, 99 or whole brain homogenates (animal models)96. But the data that may be most relevant to neuronal function is the level of small molecules in the extracellular space surrounding neurons (brain ECF) where most neuron-to-neuron signaling occurs. Currently such brain ECF metabolite data are extremely sparse. While most human CNS metabolomic data are based upon CSF samples, there is substantial evidence suggesting that CSF maybe a poor "proxy" for the levels of small molecules found in the brain extracellular space38, 49, 98, 108. Specifically, CSF small molecule levels are frequently been found to be 2-10x higher than their levels in ECF38, 98, 108 . This suggests that CSF values may often be misleading in trying to understand the composition and potential signaling impact of the small molecules in brain ECF surrounding neurons. In addition, most animal model CNS metabolomic data are based upon the use of whole-brain homogenates to determine CNS metabolites levels36, 96, 100. This whole-brain approach, however, is inadequate for determining the metabolite environment of the ECF. The intracellular space comprises ~85-90% of brain tissue while the ECF fraction



is only ~15%109. Further, most small molecules are preferentially sequestered intracellularly (typically 10x to >1000x) and are relatively excluded from brain ECF 110-112. Both of these factors lead whole-brain metabolite measures to be profoundly skewed toward detecting intracellular metabolite concentrations rather than ECF concentrations. To overcome these limitations, comprehensive, unbiased high-resolution MSmetabolomics needs to be coupled with in vivo microdialysis sampling of brain ECF36, 38, 39 . Although the extracellular space is likely the most functionally relevant signaling compartment, to date studies employing in vivo microdialysis have been largely limited to “targeted” rather than comprehensive metabolic analyses98. Regarding animal models, to date, very few studies have employed metabolomic analysis of brain ECF and our knowledge remains sparse. There are few limitations to markedly increasing this knowledge through expanded use of in vivo microdialysis MSmetabolomic analysis in animal models36, 113. Further, systematic metabolomic studies across multiple compartments (serum, CSF, whole-brain/intracellular, and brain ECF) in animal models could serve to better establish typical equilibrium ratios of a given metabolite across compartments108. Such data sets would likely permit reasonably accurate predictive estimations of metabolite levels in human brain compartments – normally inaccessible due to issues of invasiveness – based upon metabolite measures from clinically accessible compartments (e.g., serum and CSF)39, 49, 108, 114. Regarding human studies, in vivo brain microdialysis in select neurosurgical settings is currently employed to a limited yet substantial degree39. These samples, however, are currently used for targeted analyses115. When justifiable such samples could also be directed for more comprehensive metabolomic analyses. Further, when ethically, medically, and diagnostically justifiable, the frequency of in vivo microdialysis sampling studies could be safely increased substantially39. Despite these apparent opportunities, to date comprehensive MS-metabolomics of human brain ECF have not been performed36, 113. In summary, there is likely a broad role in neuropsychiatric disease risk arising from natural variations in metabolomes but resolving this role will be both technically and computationally challenging11, 51, 95, 116-118. Substantial initial progress has been made in this direction but we still have only a sparse knowledge of the full array of CNS small molecules and the spectrum of their roles in normal function14, 36, 40. While studies in humans will remain the gold standard for understanding disease, animals models serve as a relevant and efficient technical proving ground for the necessary development of enhanced metabolomic technologies96, 99, 113. Importantly, it is not unlikely that metabolite levels determined from these animal studies in many cases might be reasonably used to predict or infer human levels, although this assumption will require eventual validation on a case-by-case basis36, 92, 96. Future efforts in animal models will need to additionally address more refined questions such as how individual cellular metabolomes differ between CNS cellular subtypes and how such differences at the cellular level confer or influence the specialized function of those cellular subtypes100, 119, 120 . The genetic technologies to permit such precise isolation of distinct CNS neuronal subtypes currently exist and simply await such efforts121. As such – even employing current metabolomic technologies – we are far from exhausting their utility in revealing fundamental aspects of the CNS metabolome but this will require sustained concerted effort36, 40, 46, 92, 93, 104, 113, 117. Summary and Conclusions It is likely that both frank derangements and subtle alterations in endogenous CNS metabolites play a significant contributory role in neural dysfunction in neuropsychiatric disease11, 16, 18, 19, 44, 45, 95, 100. Our recent studies revealing that in


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hyperprolinemic disease the structural mimicry of GABA by L-proline causes GABA production deficits leading to precise alterations in GABA-ergic short-term synaptic plasticity and synchronized network activity indicate the validity of the “neuroactive metabolite” hypothesis in rare disease dysfunction6, 7, 85. Rare diseases, like hyperprolinemia, in which the levels of a single metabolite are markedly altered represent tractable, simple models in which interrogation of possible mechanisms of neural dysfunction is relatively straight-forward18, 19, 122. Despite this fact, these rare diseases remain understudied and very few disease mechanisms have been convincingly described18, 19. Whether the more ubiquitous and complex variations between individual CNS metabolomes arising from natural allelic variation – as well as from other potential sources – broadly contribute significantly to the etiology of more common neuropsychiatric diseases remains to be determined11, 12, 45, 47, 95, 100. The complex polygenic and multifactorial nature of most human diseases, however, suggests this likelihood. To address this inherently complex question of disease risk will require comprehensive metabolomics studies in large populations coupled with parallel genomics or additional mutil-omics approaches13, 92-94, 116, 123. In principle, a systematic, bottom-up approach similar to the one that enabled us to reveal the disease-relevant targets of L-proline and the resulting neural dysfunction can likely be effectively employed to reveal the precise CNS targets of neural dysfunction in other cases in which dysregulated metabolites likewise share substantial structural homology with native NTs7. Still, in many cases, it may be difficult to disentangle whether detected metabolomic disease-associations are causative in nature or whether they arise as a consequence of disease10, 11, 92, 95, 100, 117, 118, 124, 125. Further, using risk-associated complex metabolomic profiles to guide studies to determine down-stream, precisely defined disease mechanisms may often prove to be far from straightforward10, 36, 92. Despite these limitations, such disease-associated metabolomic profiles will likely permit identification of both early diagnostic and predictive biomarkers that could facilitate early detection and intervention to potentially halt disease progression10, 126. Author contributions G.W.C and J.A.G. wrote and edited the manuscript. Funding This work was supported by NIH grants R21MH10069 and R01MH096274 (to J.A.G.) as well as by grant T32MH018264 (to G.W.C.). Notes The authors declare no competing financial interests. ACKNOWLEDGMENTS The authors thank the members of the Gogos lab for critical feeback on the manuscript.



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Sources of metabolite variation Category

Mechanism Involved

Magnitude of metabolite change

Ref.

Rare Genetic Disease (IEM)

Mutations in single genes encoding enzymes that lead to dramatic reduction in enzyme activity or expression

Usually dramatic but also cases of more subtle changes.

7, 16, 18, 19, 41-43

Common & Rare Genetic Variation

Usually subtle (yet a broad range of possible) alterations in metabolite levels arising from natural allelic variation simultaneously present in multiple genes regulating metabolite levels. (with stochastic allelic synergies increasing disease risk). Rare alleles could be at times be severe.

Usually subtle but broadness of allelic variation may lead to alterations in many metabolite levels simultaneously leading to complex, unpredictable impact on CNS function. A range of effects on metabolite levels. Rare alleles could lead to dramatic increases.

20, 21, 44-47

Direct Allelic variation in metabolic enzymes and transporters

Alterations in genes that encode enzymes and proteins that directly control metabolite levels and distributions.

Usually subtle, broad alterations in metabolite levels but more marked alterations possible with certain metabolites. A range of effects on metabolite levels. Rare alleles could lead to dramatic increases.

20, 21, 46

Indirect Allelic variation in regulators of metabolic enzymes and transporters

Alterations in genes that regulate expression, activity, stability, or distribution of an enzymes directly regulating metabolite levels.

Mostly a hypothetical disease construct at present with subtle yet broad effects on metabolite levels expected. A range of effects on metabolite levels. Unlikely to lead could lead to dramatic increases.

45, 46

Blood brain barrier (BBB) disruption

Usually transient (but in some diseases chronic) alterations in BBB permeability that lead to peripheral metabolite levels influencing brain levels.

Can be substantial as peripheral metabolites are often 10 to >100x higher than their levels in brain ECF.

48-50

Microbiome variation

Variations between individual microbiomes or acute changes in microbiomes (e.g., during ABX therapies).

Variations in microbiomes leading to variations in microbiome small molecule production. Usually subtle, occasionally substantial.

50-54

Direct Alteration of microbiome products acting directly in CNS

Variations or alterations in microbiome production of BBB permeable small molecules that can act directly within the CNS to alter normal function.

Mostly a hypothetical disease construct at present but enhanced production of even small amounts of rare, atypical, or novel neuroactive products with BBB permeability could have dramatic impact on CNS function.

50-52

Indirect Alteration of microbiome products acting indirectly to influence CNS function

Alterations in microbiome products acting at gut-brainaxis targets peripherally but influencing CNS function indirectly.

Current descriptions suggest that most alterations would be subtle but the possibility for marked metabolite alterations is not unprecedented.

52-54

TABLE 1 Summary of possible sources of metabolite variation that could impact central nervous system function. Abbreviations: IEM, inborn errors of metabolism; CNS, central nervous system; BBB, blood-brain barrier; ECF, extracellular fluid; ABX, antibiotic.


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FIGURE 1 Chemical structures of (1) GABA, (2) L-proline, and (3) L-glutamate. Note GABA and Lproline share the absence of the second carboxylate group of L-glutamate.



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Diseases with structurally related metabolites sharing neuropsychiatric symptoms Disease

Gene

Pathway

Metabolite

Symptoms

Ref.

Hyperprolinemia type I (and 22q11.2DS)

PRODH Proline Dehydrogenase

Proline degradation

L-proline

Seizures Psychosis Schizophrenia

17, 2022, 46, 61, 65, 66

Acute intermittent porphyria

HMBS/PBGD Porphobilinogen deaminase

Porphyrin/Heme Biosynthesis

δ-aminolevulinic acid

Seizures Psychosis

19, 41

Pyridoxine (B6) dependent epilepsy

ALDH7A1 Antiquin Aldehyde dehydrogenase 7 family member A1

Lysine degradation

L-pipecolic acid

Seizures

18, 43

Glutathione synthetase deficiency

GSS Glutathione synthetase

Glutathione synthesis

5-oxo-proline (pyroglutamic acid)

Seizures Psychosis

42

TABLE 2 Summary of rare diseases with overlapping neuropsychiatric symptomology in which accumulated metabolites share structural similarity to L-proline. (See Figure 2).


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FIGURE 2 Structural similarity of disease metabolites from rare diseases with overlapping neuropsychiatric symptomology (See Table 2). Chemical structures of (1) GABA, (2) Lproline, (4) L-pipecolate, (5) δ-aminolevulinic acid, and (6) (S)-5-oxoproline (Lpyroglutamic acid).



FIGURE 3 Summary of major findings in mice with elevated CNS L-proline (Prodh-null). Prodh-null mice show deficits in sustained HF GABA-ergic transmission arising from GABA production deficits due to L-proline competitive blockade of GAD. Deficits in gamma oscillations are attributable to deficits in HF GABA-ergic transmission. Abbreviations: GAD, glutamate decarboxylase; L-Glu, L-glutamate; L-Pro, L-proline; GABA, γaminobutyric acid; WT, wild-type; HF, high-frequency.


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ACS Chemical Neuroscience 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

TABLE 3 Summary of GABA-system component functions and effects of L-proline. Right: GABA system components, function, and relevance. Left: effects of L-proline at GABA-system components and interpretation of disease relevance. Abbreviations: Ephys., electrophysiology; Schz., schizophrenia; ECF, extracellular fluid; R’s, receptors; usu., usually; EC50, half maximal effective concentration; VGCaCs, voltage-gated calcium channels; Kir, inwardly-rectifying potassium channel; GIRK, g-protein-coupled inwardly-rectifying potassium channel; GAT-1, GABA transporter 1; NT, neurotransmitter; GAD67/GAD65, glutamate decarboxylase 67, 65; Rxn’s, reactions; IC50, half maximal inhibitory concentration; GABA-T, GABA transaminase; [GABA]cyto, cytosolic GABA concentration.


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ACS Chemical Neuroscience 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

FIGURE 4 Flowchart of work-flow for systematic interrogation of a disease-associated metabolite with suspected neuroactivity at NT binding sites. Abbreviations: NTs, neurotransmitters; Sx, symptoms; [metabolite]CNS, metabolite concentration in CNS; ECF, extracellular fluid.


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Role of Endogenous Metabolite Alterations in Neuropsychiatric Disease

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