Source: https://www.nature.com/articles/s41538-017-0002-4?error=cookies_not_supported&code=d1238c8a-671b-4455-b709-2b33f85af747
Timestamp: 2019-04-24 22:35:11+00:00

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The processes that define mammalian physiology evolved millions of years ago in response to ancient signaling molecules, most of which were acquired by ingestion and digestion. In this way, evolution inextricably linked diet to all major physiological systems including the nervous system. The importance of diet in neurological development is well documented, although the mechanisms by which diet-derived signaling molecules (DSMs) affect cognition are poorly understood. Studies on the positive impact of nutritive and non-nutritive bioactive molecules on brain function are encouraging but lack the statistical power needed to demonstrate strong positive associations. Establishing associations between DSMs and cognitive functions like mood, memory and learning are made even more difficult by the lack of robust phenotypic markers that can be used to accurately and reproducibly measure the effects of DSMs. Lastly, it is now apparent that processes like neurogenesis and neuroplasticity are embedded within layers of interlocked signaling pathways and gene regulatory networks. Within these interdependent pathways and networks, the various transducers of DSMs are used combinatorially to produce those emergent adaptive gene expression responses needed for stimulus-induced neurogenesis and neuroplasticity. Taken together, it appears that cognition is encoded genomically and modified by epigenetics and epitranscriptomics to produce complex transcriptional programs that are exquisitely sensitive to signaling molecules from the environment. Models for how DSMs mediate the interplay between the environment and various neuronal processes are discussed in the context of the food–brain axis.
The wonders of the modern human brain can be traced to its humble beginnings. Starting with a brain of approximately 470 ml in the Hominini,30 the human brain has grown to about 1350 ml over the past 2 million years.31 The near tripling in the size of the human brain is the result of many factors not the least of which are the external inputs of energy and the molecular building blocks provided by macronutrients (e.g., proteins, carbohydrates and lipids). Although macronutrients are essential for energy and the assembly of neural and non-neural tissues, it is also likely that the micronutrients, biotransformed nutrients, phytochemicals and even anti-nutrients and xenobiotics (i.e., DSMs), triggered the plethora of molecular processes required for the growth, development and differentiation of the modern brain and all its parts. When stimuli from DSMs are integrated neuronally and subjected to the pressures of natural selection, cellular responses can emerge that produce higher-order cognition that is adaptive, sustainable and knowledge generating. This is almost assured considering the extensive feedforward/feedback regulatory controls at play in the brain as discussed later.
Since AHN was first discovered in the mammalian brain by Altman and Das,32 it was often considered a phylogenetic reversion, away from lifelong neurogenesis, in favor of neurological stability within the complexity of the brain.33 We now know that AHN occurs throughout the animal kingdom and while humans have fewer neurogenic zones than fish, within these neurogenic zones, substantial and highly functional neurons can be produced. This suggests that at least for the mammalian dentate gyrus, evolution has moved toward neurogenic plasticity rather than away from it.33 This idea is supported by recent studies using imaging connectomics34 and graph theory showing that normal brain maturation, from infancy to adulthood, involves significant co-evolution and integration of structural (neurons and glial cells) and functional (cognitive processes) networks.35 We propose that this co-evolution of neuronal and synaptic plasticity is supported, if not driven, by the constant interplay between the brain and external stimuli from food. This concept is consistent with the ecological intelligence hypothesis for primate brain evolution.36 According to this hypothesis, “foraging cognition” involving spatial memory, value-based decision making and inhibitory control creates those dynamic feedforward and feedback interactions that are adaptive and lead to higher-quality foods, more productive food sources and larger brains.36,37 We believe the principles of the food–brain axis described below, complement and extend the ecological intelligence hypothesis by connecting the nutritional environment with neurological structures and processes through well-studied signaling pathways and gene regulatory networks.
In recent years, it has become apparent that all physiological, metabolic and genetic processes and systems are interconnected and interdependent to some degree. Examples include the inflammation–immunity axis,38 the hypothalamus–pituitary–gonadal axis39 and the gut–brain axis.28,29 In each case, an axis suggests diverse regulatory lineages, orchestrating control and outcomes over other critical processes. This makes all the systems involved highly sensitive to extracellular signals including those from the environment. The food–brain axis represents more than just connectivity and relatedness between what we eat and how our brain grows and functions; it illustrates dynamic interdependencies between food and neurological processes. This semi-quantitative interpretation of “axis” should enable researchers to categorize, quantify and predict neurological changes as a function of food quality and/or quantity.
Here, the food–brain axis is defined as a horizontal line of independent variables thought to be causative (e.g., food) that transects a vertical line of dependent variable thought to be the effects (e.g., neurogenesis, neuroplasticity and neuropathologies). Figure 1 shows three hypothetical examples (a, b and c) depicting the consequences of changing the quantity and/or quality of the food from poor (e.g., -3) to good (e.g., +3) on the X-axis, and its effect on neural growth, differentiation and function on the Y-axis. In addition to being an informative method for displaying the dynamic relationship between diet and brain structure and function, the food–brain axis organizes these interactions into four quadrants (i.e., i–iv). Figure 1b shows the nominal or neuro-typical condition for food–brain interactions whereas Fig. 1a-iv shows a neuro-atypical/challenged condition in which neurological processes are dysfunctional, degenerative and pathological as a result of poor diet. Conversely, Fig. 1c-ii illustrates a neuro-atypical/enhanced condition in which neurological processes and structures are enhanced in response to dietary inputs that are higher in quality and/or quantity.
The neuro-atypical quadrants A-i and C-iii pose interesting questions about food–brain interactions. In the case of quadrant A-i (neuro-atypical/enhanced) one might surmise that brain development and function (as defined by measures of intelligence) may be more dependent on robust genetic factors and age than on the quantity and/or quality of food intake. For example, it is known that the genetic contribution to human intelligence is approximately 80% for adults with additive genetic variance contributed by selective mating based on similar phenotypes.40 Therefore, if prenatal and early postnatal nutrition are adequate (e.g., nursing), the impact of nutritional deficiency later in life may have little or no measurable effect on cognitive performance. For quadrant C-iii, strong genetic determinants like Fragile X syndrome, Huntington’s disease, PKU as well as traumatic brain injury (TBI; Box 1) come into play. These are conditions that make neurological structures and processes refractory to the benefits of abundant, higher-quality dietary inputs. PKU, an autosomal recessive metabolic disorder, causes a toxic accumulation of dietary phenylalanine in the brain. If undiagnosed and left untreated, PKU can cause serious cognitive impairment, behavioral and mental disorders as well as seizures, regardless of food quality and/or quality. However, nutritional intervention with a low phenylalanine diet supplemented with large neutral amino acids, vitamin D and B12 can prevent or mitigate the physical, neurological and developmental problems associated with PKU.41 Therefore, interpreting the impact of food quality and/or quantity (x-axis) in quadrant A-i, age must be taking into account while in quadrant C-iii, early diagnosis of deleterious genetic factors or structural damage to brain tissues are key factors for consideration.
The impact of food on brain development and function has been extensively reviewed, although these reviews are often more descriptive than mechanistic in nature.15,54,55 They emphasize the neuroprotective aspects of nutrition but seldom discuss the mechanistic role of DSMs in AHN and cognition. Some of the first to systematically review research on the mechanistic effects of DSMs on the brain were Gomez–Pinilla55 and Zheng and Berthoud.56 A list of some of the dietary factors discussed in these reviews is shown in Table 1. While not an exhaustive list, Table 1 underscores the multifunctional character of DSMs that range from sources of energy and building blocks for cellular structures to chemical signals that trigger gene regulatory cascades that control transcriptional programs in the brain. One of the best examples of a multifunctional DSM is the long-chain polyunsaturated fatty acid (PUFA), DHA, which can serve as a nutrient, transcription regulator, immuno-modulator and neurotransmitter.
Reduced brain or circulating DHA concentration has been implicated in depression, bipolar disorder and attention deficit (AD) disorder.57,58,59 However, intervention studies with long-chain omega-3 PUFAs have yielded mixed results.5,57,58,59,60 One recent meta-analysis, however, suggested an overall beneficial effect for EPA in major depressive disorder patients, especially at high doses.61 Interestingly, the beneficial effects of EPA were also observed in subjects taking antidepressants. Whether the beneficial effects of high EPA dosage together with antidepressants are additive or synergistic can have significant therapeutic implications and thus requires further study. Most studies designed to assess the benefits of omega-3 PUFAs on children with AD disorder are inconclusive. Another recent study, however, showed significant improvement in working memory for children with attention deficit hyperactive disorder supplemented with EPA62 (see Box 2).
For DSMs to impact various neurological structures and functions in ways that produce neurogenesis, synaptic plasticity and adaptive behaviors, there must be an efficient communication system allowing dietary stimuli to be delivered to the brain from the gut. These connections are provide by the 400–600 million neurons in the human enteric system77 that creates a virtual information highway through which DSMs can communicate critical chemical information from the environment to the brain.78 Alternatively, oxygen and nutrients in peripheral blood can be delivered to the brain via the middle cerebral arteries and their fenestrated capillaries to support hippocampal6 and hypothalamic functions.79 These communication channels permit dietary inputs to be more than just fuel and building blocks for the brain, but also a means for delivering important chemical signals from the extracellular environment to the neuron where they are continually integrated into those signaling pathways and neuronal activity needed for metabolic homeostasis, cognition and overall health.55,56,79 Box 3, Box 4.
One of the challenges to understanding a nucleic acid-based model for cognition is the temporal scales that can span several orders of magnitude between stimulus sensing and experience-dependent neuronal plasticity. For example, the time to elicit an electrophysiological signal from a neuron is in the range of microseconds, while the initiation of transcription and translation of RNA can span minutes to hours. On the other hand, the time required for learning and durable memory might take days to years.85 This temporal discordance makes the trajectory between stimulus sensing and neuronal plasticity non-linear and difficult to understand.
One possible way to resolve these manifold differences in timing between sensing and response is to view them in the context of molecular processes that affect the rate and magnitude of each step along the trajectory. These molecular processes include epigenetic and epitranscriptomics modifications of chromatin, DNA and RNA (e.g., altered RNA structure, half-life, localization and ligand affinity and, methylation of histones, DNA and RNA85,86,87) and homeostatic scaling.86,88 All of these processes help create the diversity of transcription programs required for proper neuronal function. In the case of homeostatic scaling, both the proteome88 and transcriptome86 are altered to adjust synaptic strength up or down in response to changes in inputs. Deficiencies in homeostatic scaling are associated with neurological disorders such as autism spectrum disorder, epilepsy, Parkinson’s and schizophrenia and underscore the need for tight control over network activity for proper neuronal function.88 Interestingly, many of the transcription factors (e.g., CREB, Elk1, SRF), kinases (e.g., CaMK, CDK5, MAPK) and growth factors (e.g., BDNF) associated with homeostatic scaling are also components of pathways (e.g., ERK) that crosstalk with neurogenesis signaling (Fig. 2, Transactivation of Signaling Pathways, Supplemental Table 1 A comparison of signal transduction pathway relative of signaling for glucose signaling).86 In terms of the potential impact of inputs like DSMs on homeostatic scaling, expression of BDNF, a neurotrophic factor involved in neurogenesis, memory and learning, can be triggered by a variety of polyphenolics compounds found in plant-based foods (Table 2).
Over the past decade, the toolbox for regulating gene expression in the mammalian brain has expanded to include long-noncoding RNAs,89 enhancer RNA90 and long-lived circular RNAs.91 These regulatory RNAs also have the potential to be modified by epigenetics and/or epitranscriptomics to regulate neuronal behaviors, synaptic scaling, plasticity and ultimately cognition.
The dynamics of the food–brain axis can also be bidirectional in that food intake can regulate the expression of genes involved in memory, learning and adaptive behaviors, while adaptive behaviors, learning and memory can regulate gene expression to change food intake. This neuronal bidirectionality is best illustrated by the peptide hormones leptin, ghrelin, insulin and nesfatin-1 that use nutrient sensing to regulate satiety, hunger and food reward signals in the brain.92,93 When expressed or injected into rat brains, for example, nesfatin-1 can create anorexia in rats by inhibiting food intake, modulating the excitability of glucose sensitive neurons, and acting on the melanocortin system to enhance UCP1 expression in brown adipose tissue.93 It is the bidirectional nature of activity-dependent changes in neuronal function that holds promise for dietary interventions designed to create those adaptive and positive self-managing behaviors that contribute to good mental health.
Biological systems are parsimonious in terms of achieving adaptive responses to changes in the environment. As mentioned above, the brain draws upon efficient and well-developed molecular processes like nutrient sensing, transactivation of signaling pathways (i.e., signaling crosstalk) and stimulus-specific combinatorial gene regulation to support the neural growth, differentiation and experience-dependent neuronal plasticity.56,103,104 To see how transactivation of signaling is achieved one need only compare the components of various signaling pathways to a critically important pathway such as glucose signaling (Fig. 2).
As can be seen in Fig. 2, the potential for crosstalk between different signaling pathways is considerable. This suggests that any DSM, with even modest affinity for one of the signaling proteins in this limited list, has the potential to transactivate other signaling pathways to create interlocking pathways capable of linking neurogenesis to nutrient sensing. By using transactivation to interlock signaling pathways, even small fluctuations in the concentration of any component of a pathway can produce rapid, nonlinear and dynamical shifts in pathway outcomes.
Essential to producing emergent adaptive behaviors by neurons is the requirement for large amounts of informational input (i.e., DSMs) from the environment126,129 and a “decision space” where inputs are turned into outputs (i.e., solutions). Because of the large number of decisions that must take place in a complex system like the brain, input must be abundant and diverse. In the context of the food–brain axis, this means that dietary input should be rich in non-nutritive bioactive molecules (e.g., polyphenolics) as well as nutritionally dense macronutrients (e.g., carbohydrates, amino acids, omega-3 PUFAs). As shown in Fig. 1, changing the quality and/or quantity of dietary input can change the set point in the food–brain Axis to produce experience-dependent changes in the brain that are either advantageous or deleterious to cognitive health.
While reports on the ability of DSMs to impact neural development and function are many, it is more challenging to identify the signaling proteins through which their influence is exerted. This is due in part to the complexity of signal transduction networks regulating neuronal identity and activity. Two of the best examples of this complexity are the transcription factors CREB and c-Fos, which play essential roles in long-term modulation of neuronal activity.115 A series of signaling pathways including cAMP/PKA, Ras/ERK, Ca++/CaMK, and PI3K/Akt converge to regulate CREB and c-Fos gene expression.115,116 This combinatorial control allows neurons to make long-term changes in their activity profile in response to both membrane depolarization and hormonal inputs. However, recent work in other cell types has revealed that this core set of pathways is also integrated with metabolic signaling. The Ras/ERK cascade is mutually antagonistic with AMPK, a kinase that becomes active when cellular ATP levels fall, through both regulatory phosphorylation117,118 and through mutual interactions with the scaffold protein KSR2.119 Similarly, PI3K/Akt signaling is tightly intertwined through both positive and negative feedbacks with the mTOR kinase complexes,120 which are highly sensitive to amino acid availability.121 Beyond these interconnections, this network can be expanded to include many more components and feedback loops.122 Thus, macronutrient availability presumably influences the decision of neurons to activate CREB and c-Fos, and thereby alter their activity profile. This implication has yet to be explored in detail but an emerging body of research suggests that DSMs do indeed impact the CREB and c-Fos signaling networks in the neuron.123,124 The elaborate feedback structures of these networks create a formidable barrier to understanding signaling in the neuron and necessitate a systems-level approach to unambiguously identify the molecular mode of action for DSMs.
Finally, strategies for enhancing cognition and therapies for treating neuropathologies that include evidence-based nutrition should become more common as we learn more about the food–brain axis. This will require a multiscale, top-down approach that includes diverse data sets that span different locations, scales (e.g., tissues, cells and molecules) and time points to reveal the underlying connectivity, interdependencies and adaptiveness of biological systems like the brain.150,151 From nutrient to nucleotide to neuron, all scales must be analyzed vertically and orthogonally to fully understand the molecular basis of cognition in all its forms and dysfunctions. Accomplishing this daunting task will require collaboration and communication across multiple disciplines like food science, nutrition, genomics, molecular biology, neuroscience and informatics. Such transdisciplinary approaches are not only vital to understanding food–brain axis but other complex biological systems as well. Recognizing the importance of DSMs in signaling those transcription programs needed for neurogenesis and activity-dependent changes in neuronal functions is an important first step.
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The authors want to thank Taekyung (T-K) Kim, University of Texas, Southwestern Medical Center and Darshan Kelly, ARS-USDA Western Human Nutrition Research Center (NIFA Project Number CA-D-MCB-5896-H) for their helpful advice and encouragement. We are also indebted to Peter C. Wainwright, former interim dean of the UC Davis College of Biological Sciences for his encouragement and support of the Neuro-Nutrition Workgroup from June 2016 to June 2017. Finally, we want to acknowledge the President’s Council of Advisors on Science and Technology (PCAST) for the enthusiasm they expressed for this topic at its September 28, 2015 meeting in Washington D.C.
All authors contributed equally in developing the material for this article and for critically reviewing the manuscript and approval of the final draft. R.L.R. conceived the theme of the article based on his participation in the PCAST meeting of September 28, 2015.
Correspondence to Raymond L. Rodriguez.

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