JBR Journal of Translational Biomarkers & Diagnosis (JBR-TBD)  /  JBR-TBD-01-101

Metabonomics, Brain Apoptosis, and Carbon Monoxide

Vicki L. Mahan

Assistant Professor, Department of Surgery, Division of Pediatric Cardiothoracic Surgery, Drexel University College of Medicine, St. Christopher’s Hospital for Children, USA.

*Corresponding Author

Vicki L. Mahan MD,
Assistant Professor,
Department of Surgery, Division of Pediatric Cardiothoracic Surgery,
Drexel University College of Medicine, St. Christopher’s Hospital for Children,
3601 A Street, Philadelphia, PA 19134,
E-mail: Vicki.mahan@tenethealth.com

Article Type: Review Article
Recieved: August 09, 2015; Accepted: August 31, 2015; Published: September 03, 2015

Citation: Vicki L. Mahan (2015) Metabonomics, Brain Apoptosis, and Carbon Monoxide. 01(1), J Translational Biomarkers Diagn. 1-8.

Copyright: Vicki L. Mahan© 2015. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.


Metabonomics tell us how an organism has responded to a stimulus or challenge and is an indicator of cell physiology and response to stress. This systematic study of unique chemical fingerprints has resulted in quantitative data on a broad range of metabolites reflecting metabolism and/or metabolic shifts associated with brain health, pathology and/or treatment. Complex interactions between nutrition, genetics, and the central nervous system (CNS) determine optimal cerebral energy state and are mediated by changes in energy needs, metabolism, environment and multiple signaling molecules. Evaluation of exogenous administration of normal endogenous neuroprotective molecules may be expeditious and succinct. The normal physiologic effects of one of these agents, carbon monoxide (CO) - a major neurotransmitter and gasotransmitter, is important for multiple neurologic functions and neuroprotection and its role as a neuroprotective and neurotherapeutic agent has been suggested. Metabonomic profiling will provide further insight into the role of CO in CNS health and will add greatly to our ability to improve the quality of life in our patients with neuropathologic, neuropsychiatric, and neurodegenerative disorders.

3.Metabonomics and the Brain
4.Metabonomics and Brain Apoptosis
5.Carbon Monoxide and Metabonomics
6.Relationship of Carbon Monoxide to Apoptosis
7.Carbon Monoxide and Nutrition
8.Effect of Extrinsic Carbon Monoxide on Neuropathology


Metabonomics of the Brain and CNS; Metabonomic Profiling; Carbon Monoxide as a Neurotransmitter; Gasotransmitters.


The brain’s health and response to stress are dependent on genomics, transcriptomics, and proteomics. Complex interactions between nutrition, genetics, and the central nervous system (CNS) determine optimal cerebral energy state and are mediated by changes in energy needs, metabolism, environment and multiple signaling molecules. Metabolites constitute substrates and products of the biochemical reactions, are the metabolic signature of biochemical activity and reflect the phenotype of the cell and/or organism [1]. Metabolomics, “the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification” and determined by quantification of the metabolites, tell us how the organism has responded to a stimulus or challenge and is an indicator of cell physiology and response to stress. Metabonomics is the fingerprint of biochemical perturbation caused by disease, drugs, and toxins [2, 3]. Correlation of biochemical changes with phenotype linking cellular pathways to biological mechanisms by using the resulting metabolome provides a powerful tool to assess unexpected biochemical pathways respondent to injury and treatments [4]. This is the most sensitive measure of a cellular phenotype.

Assessed by metabolomic technology, analysis of these smallmolecule metabolites can be used to understand the changes in homeostasis in the biological system [5-9]. Single cell metabolomics is being done using mass spectrometry, microfluidics, and capillary separations [10, 11]. Systematic study of these unique chemical fingerprints has resulted in quantitative data on a broad range of metabolites reflecting metabolism and/or metabolic shifts associated with brain health, pathology and/or treatment [12-23]. With brain injury, there is a change in protein and mRNA expression which is observed hours, days, or weeks after the insult, but metabolomes provide the clinician more timely assessment of the extent of injury as well as response to therapy.

Application of metabolomic technologies to assess neurotherapies would improve the understanding of disease and may help to limit progression of disease and/or allow quantification of reversal of injury. Evaluation of exogenous administration of normal endogenous neuroprotective molecules may be expeditious and succinct. One of these molecules, carbon monoxide (CO) - a major neurotransmitter and gasotransmitter, is important for multiple neurologic functions [24-34]. Agents affecting the synthesis, transactions, and disposition of the gas have clinical relevance to neuroprotection [35-46]. Exogenous administration of inhaled CO or carbon monoxide releasing molecules (CORM’s) impart similar neurophysiological responses as the endogenous gas and dose and duration of exposure is important. Currently, the drug is under development as a therapeutic agent and safety studies in humans evaluating the safety and tolerability of inhaled doses of CO show no clinically important abnormalities, effects, or changes over time in laboratory safety variables. As an important therapeutic option, CO has entered clinical trials and its clinical role as a neuroprotective and neurotherapeutic agent has been suggested. CO, like nitric oxide (NO) before it, may prove to be a therapeutic option as a new and novel approach to various neuropathologies. The role for CO as a neurotherapeutic based on compelling animal data necessitates further testing in humans. The time has come to assess this simple gas as one cannot ignore the remarkable data that continues to be reported. Metabonomics to assess effectiveness of this gasotransmitter in treating brain pathology and injury would greatly benefit patients by providing timely information on both the extent of damage and effects of treatment.

Metabonomics and the Brain

Cell precursors of the brain begin to develop early in embryogenesis. An ordered sequence of temporally and spatially related morphogenetic events are under the control of specific genes and the molecular mechanisms which underlie normal brain function and development as well as pathology [47-52]. This continues and changes through life. The dynamic and varying metabolomic signatures in the brain reflect both the health of neural tissues at various stages of development and disease [53-61]. These depend on brain vulnerability which relates to whether an agent reaches the nervous system and the time of exposure and may not be apparent immediately. The many chemical modulators of nervous system damage can trigger a sequence of events resulting in neuronal damage and cell death. Resulting injuries depend on brain maturity and cellular health as well as location of the lesion. A chain of events leading to apoptotic DNA fragmentation, cellular fragmentation and engulfment of the cell may result. Salvage and/or the recovery/regeneration of the nervous system, its cells, structure and function may be varied due to the area of the CNS involved, health of the cells/organ, and treatments. Biochemical changes may be normal findings and resulting metabolomes must, therefore, be compared to what is normal for age and development and evaluated in the context of clinical findings [62-70].

However, normal clinical findings do not exclude neuropathology. Neurologic diseases are common as millions of people suffer from neurodegenerative diseases, traumatic brain injury (TBI), and psychiatric illnesses, but timely diagnosis and indicators of outcome have been limited. Diagnosis and successful intervention have been determined by symptoms and scales. Inaccurate and imprecise, these clinical approaches do not address biochemical processes responsible for the recognized pathology. Clinicians have routinely used computed tomography (CT) and magnetic resonance imaging (MRI) to evaluate the extent and type of neurologic disease, but these studies are less sensitive for diagnosing acute injury or response to therapy [71-79]. When changes are identified on CT or MRI, cells have already undergone apoptosis or necrosis. The serious metabolic disorders of neuronal and non-neuronal cells occur earlier than reflected by these studies and specific regions of the CNS show differential vulnerability. Rapid assessment of injury and treatment before clinical manifestations are apparent could improve long-term outcomes and limit pathology by allowing earlier clinical intervention. Transcriptomic approaches provide information on gene expression, but may not reflect physiological processes and proteomics may not be predictive of biological responses [80, 81]. Quantitative analysis of metabolites is able to detect the presence or absence of thousands of small molecules, but no single technique measures the complete metabolome. Mapping of the regulation of neural metabolic pathways using metabonomics will help identify the type of pathology and suggest strategic points of therapeutic intervention in patients with neurologic insults.

Metabonomics provides more timely information and has been applied to the CNS [81, 82-93]. Biofluid evaluation of key metabolites in plasma and whole blood, serum, urine, saliva, cerebrospinal fluid, synovial fluid, semen, and tissue homogenates has characterized clinical status and response to treatment in several diseases [94].

Metabonomic techniques to determine CNS health include in vitro technologies as well as in- or ex- vivo approaches. Approaches include metabolite target analysis (analysis restricted to metabolites of a particular enzyme system that would be directly affected by abiotic or biotic perturbation), metabolite profiling (analysis focusing on a group of metabolites), metabolomics (comprehensive analysis of the whole metabolome under a given set of conditions), metabolic fingerprinting (classification of samples on the basis of provenance of either their biological relevance or origin), metabolic profiling (commonly used in clinical and pharmaceutical analysis to trace the fate of a drug or metabolite), and metabonomics (measure of the fingerprint of biochemical perturbation caused by disease, drugs, and toxins) [95]. Analyses measure a subset of the whole profile with little differentiation or quantitation of metabolites, assess the metabolic profile within or associated with a particular metabolic pathway, and/or focuses on a particular segment of the metabolome by analyzing only a few selected metabolites that comprise a specific biochemical pathway. Results are context-dependent and change depending on the physiology, pathology, and developmental state of the cell, tissue, organ, and/or organism. The types of databases that are useful to interpret this information include databases storing detailed metabolite profiles, single species-based databases, databases storing complex metabolite profile data from many species in many different physiological states, databases listing all known metabolites for each biological species, databases compiling established biochemical facts, and databases that integrate genome and metabolome data with an ability to model metabolic fluxes [95].

Systems-biology approaches to the normal and abnormal CNS using metabolomics purports a metabolism-based mechanistic understanding of the metabolic and resulting physiologic function of disease and response to therapy [89]. Approaches quantify data on a broad range of metabolites in order to understand shifts in metabolism and biochemistry in different CNS pathologies. Instruments are able to quantitate thousands of small molecules and mathematical tools identify molecular signals for a specific disease. The two most accepted methods used in the measurement of metabolites are nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). The former consists of the absorption and re-emission of electromagnetic radiation by atomic nuclei in a magnetic field and is applicable to analysis of biofluids, cell extracts, and cell cultures and requires almost no sample preparation. The standard approach using patient’s samples is using proton NMR (1H NMR) although 2H, 13C, 31P, 15N, and 19F may be employed. MS-based metabolomics may provide a targeted or large-scale metabolome analysis and is usually combined with three types of prefractionation techniques – gas chromatography (GC), high-performance liquid chromatography (HPLC), or capillary electrophoresis (CE). GC is highly efficient, sensitive, and reproducible, but can only be performed with volatile compounds or those that can be made volatile. Although HPLC separation may reach a wider range of substrate that can be analyzed, its resolution is poorer. CE is applicable to charged substrate and is superior in performance regarding separation than HPLC.

Application has allowed discrimination of metabolic markers noninvasively in vivo. In animal models of asphyxia/hypoxia, accumulation and delayed recovery of Krebs cycle intermediates have been described, changes in amino acid profiles shown, and disturbances of the cell membrane illustrated [96-99]. NMR spectroscopic metabolic profiling of cerebral spinal fluid and serum identifies differences between idiopathic intracranial hypertension, multiple sclerosis, cerebrovascular disease, and mixed neurological diseases in humans. The authors concluded that metabolomics may be a clinically useful tool for diagnosis and may help identify biochemical pathways unique to a neurodisease [100]. In infants with perinatal asphyxia, Huang and colleagues measured the ratio of lactate to creatinine in urine by proton nuclear magnetic resonance spectroscopy. The authors concluded that measurement soon after birth may help identify infants at high risk for hypoxic-ischemic encephalopathy (HIE) [101]. Reinke et al characterized the NMR-derived umbilical cord serum metabolome and concluded that 4 metabolites (3-hydroxybutyrate, glycerol, O-phosphocholine, and succinate) predicted HIE severity [102]. Untargeted metabolomic LC/MS analysis on plasma of patients who were sleep deprived showed that 27 metabolites (tryptophan, serotonin, taurine, 8 acylcarnitines, 13 glycerophospholipids, and 3 sphingolipids) showed significantly increased levels compared with during sleep [103].

Neuronal and non-neuronal cells are assessed. Morphologic differences include size, number of dendrites, complexity of the dendritic tree, number and types of synapses, axonal length, and degree of axonal myelination. Differences are expressed by the chemical specificity of the neurotransmitters and by their electrical properties. Critical assessment provides knowledge of mental health and well-being, mental disorders and schizophrenia, neurological (neurodevelopmental and neurodegenerative) disorders, Alzheimer’s disease and traumatic brain injury (TBI). With aging, normal changes in metabolomic profiles and signatures are seen. Expression of genes results in metabolomic signatures unique to the metabolic properties of normal and abnormal CNS metabolism and differ in patients with neurodegenerative diseases, Alzheimer disease, Parkinson disease, amyotrophic lateral sclerosis, and traumatic brain injury as neuronal vulnerability and markers are diverse but specific to pathologies. Identification of the unique metabolome associated with neurologic diseases and/or injury would greatly help clinicians to understand the biochemical processes, possible treatment options, and response to treatment.

Metabonomics and Brain Apoptosis

Morphological traits have been used to classify cell death modalities [104]. Subtypes include accidental cell death where there is immediate loss of structural integrity in a completely uncontrollable manner and regulated cell death which is initiated by a genetically encoded pathway. The former, necrosis, is a passive process resulting in cell swelling, membrane rupture, inflammation, and depends on toxicant-specific biochemical mechanisms. Apoptosis, the latter, is an active process resulting in cellular shrinkage with an intact membrane, nuclear condensation, no inflammation and is an evolutionarily conserved pathway. Programmed cell death refers to regulated cell death that occurs as part of a developmental program or to preserve physiologic adult tissue homeostasis and the terms apoptosis and programmed cell death have been used interchangeably [105]. The system defines extrinsic apoptosis to be apoptotic cell death induced by extracellular stress signals that are sensed and propagated by specific transmembrane receptors. Caspase-dependent and caspase-independent “intrinsic apoptosis” refers to apoptotic demise of cells triggered by intracellular stress. Regulated necrosis has also been shown to be important in multiple physiological and pathological settings and is dependent on specific signaling modules. Autophagic cell death indicates cell death accompanied by massive cytoplasmic vacuololization. Mitotic catastrophe refers to cell death triggered by aberrant mitosis and executed either during mitosis or in the subsequent interphase. Anoikis describes the absence of cell-tomatrix interactions resulting in cell death. Entosis is a cell death mechanism linked to cell-in-cell phenotype frequently exhibited by non-phagocytic cells in clinical tumor samples. Parthanatos is a cell death mode involving DNA damage-responsive enzymes poly(ADP-ribose) polymerases (PARPs). Pyroptosis describes a specific type of death of macrophages infected by Salmonella typhimurium. Netosis cell death subroutine is restricted to granulocytic cells, insensitive to caspase inhibition, insensitive to necrostatin, dependent on NAPDH oxidase-mediated superoxide generation, and dependent on the autophagic machinery. Difficulties in defining the various biochemical pathways resulting in specific morphologic findings of a specific type of cell death had impaired the discovery of therapies for pathology. Biochemical methods of classifying cell death subroutines has been suggested for systematic classification of cell death. However, there must be physiopathological relevance, must correlate with genetic studies, must be specific to signaling of cell death, must reflect crosstalk between the different cell death subroutines, must define programmed versus regulated versus accidental cell death, and must be specific to a particular signaling pathway. And while cellular heterogeneity is normal in the CNS - cells may be exposed to a different microenvironment, may be in a different cell-cycle stage, may be genetically different, etc. - single neuron metabolomic analysis will improve our knowledge of similarities and differences between cells, functioning of the CNS, response to stress, and improvement with treatment.

Correlation of accepted technologies to determine the presence of apoptosis with metabonomic studies has resulted in an underestimation of pathology by the former. In a mouse model of Parkinsons’ disease using 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), there was no evidence of apoptosis in all areas of the mesencephalic dopaminergic network by TUNEL assay, but metabolomic profiling revealed 17 metabolites which were significantly altered relative to controls and an additional 13 metabolites narrowly missing the p<0.05 cutoff. Variations of the metabolites resulted in five major groups based on role and mapping of a set of metabolites into metabolic pathways. The authors concluded that there was significant modulation of metabolic pathways in the brain without causing significant nigrostriatal cellular changes with acute MPTP exposure [106]. Tsang and colleagues evaluated biochemical changes in 3-Nitroprionic acid rat model of Huntington’s Disease. Comparative morphology of neurological tissues control and study groups using H&E and GFAP brain sections showed no difference and changes indicative of apoptotic and excitotoxic cells were equally abundant in both groups. However, in the study group, metabolic changes were seen in all brain regions examined by 1H NMR spectroscopy. They concluded that the metabolic profiling technique provided a more sensitive characterization of the toxicity of 3-NP than standard histopathological criteria [107]. In a rat model of focal cerebral ischemia-reperfusion, apoptosis of hippocampus nerve cells were similar in sham versus optimized rhubarb aglycone treated rats. However, metabonomic analysis on plasma and urine metabolites showed that principle component analysis scores plots were not the same as the sham operation group [108]. Further evaluation of metabonomic profiling diagnosing apoptosis of neural cells is needed to improve our understanding of the biochemical changes resulting in pathology and possible cell death and more timely interventions resulting in improved outcomes.

Carbon Monoxide and Metabonomics

The predominant source of endogenous CO in the brain is from oxidative degradation of heme by the hemeoxygenases. Other sources include degradation of other heme proteins (myoglobin, catalase, peroxidases, and cytochromes) and lipid peroxidation. Interactions of CO with macromolecules in the brain is the result of signaling cascades which determine biologic activity (neurotransduction, transcription, vascular resistance, and metabolism). Heme proteins are the key to the generation, signal transduction and interaction of CO, nitric oxide (NO), and hydrogen sulfide (H2S) neurobiology. Functions of these proteins include gas transport, transfer of electrons, facilitation of reduction-oxidation reactions that occur at catalytic sites of specific enzymes, and sensing of gases. CO modulates ATP production, glucose metabolism, energy balance, and cellular respiration [110]. Determination of biochemical mechanisms of CO have been difficult to evaluate due to its pleiotropic nature, ability to contact rapidly with functional groups of different molecules, and change of redox state of metal centers of prosthetic groups of proteins. In vitro analysis using purified enzymes to correlate structure of heme binding pockets with catalytic reactions have helped determine gas-sensing and gas-transduction mechanisms [110-115]. in vivo studies though have been limited. Mass spectrometry with quantitative metabolomics is now able to determine metabolic footprinting in animal models with deletion of specific gas-producing enzymes to determine sites of action of the gas.

Presence of the gas is required before biochemical actions result. Permeability across membranes is by simple diffusion and/ or transport and detection is by gas sensors usually heme-based sensor proteins. Signaling is determined by oxidative states of the central iron of the prosthetic heme and the binding affinity of CO, ligand binding and base affinity, conformational changes within the protein arising from ligand binding and structural changes and protein functions. Heme iron is important in defining ligand discrimination. The ferrous oxidation state of hemoglobin preferentially binds CO. This ligand-binding causes positional changes of the distal histidine group and is a step in the signaltransduction mechanism of heme-protein sensors. Differential metabolomics suggest that CO upregulate metabolites in the remethylation cycle and down regulate those in the transsulfuration cycle. The hemeoxygenase (HO)/CO and cystathionine β-syntase (CBS) systems interface and CO can regulate the activity of CBS, an H2S-producing enzyme. The HO/CO biochemical pathway is between the tricarboxylic acid (TCA) and methionine/thiol pathway. Changes impact remethylation, transsulfuration, methionine salvage and polyamine metabolism which alters the cells response to oxidative stress [116]. Treatment directed toward these changes would impact cellular and subsequent brain physiology.

Relationship of Carbon Monoxide to Apoptosis

Involvement of CO in several aspects of neuron biochemistry and physiology suggests relevance to apoptosis and clinical outcomes. The hemeoxygenase/CO axis has been suggested as a therapeutic target for several neurodegenerative diseases. A product of heme degradation catalysed by hemeoxygenase, CO is considered important as a neuroprotective agent [30, 117-121]. Potential mechanisms of action are redox control, modulation of proliferation, and modulation of the immune system. Neuronal and non-neuronal CNS cells up-regulate HO-1 in response to stress [122]. Reactive oxygen species (ROS), involved in the development of neurodegenerative disorders, are affected by the end products of the hemeoxygenases. Upregulation of HO-2 or HO-1 induction correlates with increase in cerebral blood flow during seizures, hypoxia, and/or hypotension. HO-2 maintains autoregulation of cerebral blood flow and is a defense mechanism that blocks oxidant formation preventing cell death. Intrinsic production for homeostasis may be upregulated by different stresses and may have important roles in cellular antioxidant defense. In a bicuculline model of seizures in piglets, Parfenova and colleagues found that an HO inhibitor potentiated seizures whereas an HO-1 inducer blocked seizure activity [123]. Hung and colleagues injected adenovirus containing human HO-1 gene into rat substantia nigra concomitantly with 1-methyl-4-phenylpyridinium. The authors found that overexpression of HO-1 increased the survival rate of dopaminergic neurons, reduced the production of tumor necrosis factor alpha and interleukin-1 beta in substantia nigra, antagonized the reduction of striatal dopamine content induced by 1-methyl-4-phenylpyridinium, and up-regulated brain-derived neurotrophic factor and glial cell line-derived neurotrophic factor expression in substantia nigra. The authors concluded that HO-1 induction exerts neuroprotection [124]. Doré and colleagues demonstrated increased neuronal death in cerebellar granule cultures of HO2 (-/-) mice with a selective augmentation of apoptotic death and that HO2 transfection rescued apoptotic death [125]. P2Y13 receptor mediated activation of the Nrf-2/HO-1 axis also results in neuroprotection. The former binds antioxidant response elements and regulates transcription of detoxification genes. Nrf2 activation induces HO-1, important to cellular defense against oxidative stress [126, 127]. In human immunodeficiency virus (HIV) infection of the brain, HO-1 expression is decreased and is associated with the release of neurotoxic levels of glutamate. HO-1 induction has been suggested as a therapeutic strategy for neuroprotection against HIV infection [128]. However, up-regulation of hemeoxygenase has also been associated with apoptosis [37, 129-134]. Why this occurs is not clear. Determination of metabonomic signalling due to hemeoxygenases will improve our understanding of the different pathways associated with the hemeoxygenase/CO axis and, thus, improve our clinical treatment of the biochemical derangements seen with the neuropathologies.

Carbon Monoxide and Nutrition

Calorie restriction and diets rich in antioxidants may delay aging and neurodegenerative diseases. Nutritional manipulation of vitagenes and, therefore, the HO/CO axis, may promote activation of cytoprotective genes and down-regulation of proinflammatory and pro-oxidative genes. Nutrition impacts intracellular NAD/ NADH ratio regulating a group of proteins linked to metabolism and stress intolerance in multiple organisms. Multiple metabolic abnormalities with excessive production of reactive oxygen species (ROS) and oxidative stress can result in “protein conformational diseases” including amyotrophic lateral sclerosis, Parkinson’s disease, Alzheimer’s disease, and Friedreich ataxia [135]. Nutritional antioxidants have been shown to activate vitagenes which encode for HO-1 and are important for counteracting oxidative and nitrosative stress [136]. The HO-CO axis modulates the neuroendocrine mechanism of stress. Increases in CO production exerts biological effects through the activation of the cytosolic form of guanylyl cyclase (sGC) resulting in increased intracellular cGMP [137]. Activation of cyclooxygenase (COX), large-conductance Ca2+ -activated K+ (KCa) channels, and modulation of the p38 MAPK-signaling pathway are alternative intracellular signal transduction pathways resulting in neuroprotection [138-140]. Further study is necessary before developing recommendations for diets to prevent aging and neurodegeneration by nutritional activation of vitagenes and the HO/CO axis.

Effect of Extrinsic Carbon Monoxide on Neuropathology

Carbon monoxide is neuroprotective in numerous small animal models of brain injury [141-145]. Safety and tolerability studies using inhaled CO have been completed in adults and are in progress in neonates. Clinical application could be as a preconditioning agent, postconditioning agent, or periconditioning agent. Inhaled CO has entered clinical trials (www.clinicaltrials.gov) and is an important therapeutic option. Although the role of CO in the brain has historically been negative, current data suggests that the drug may be an important therapy for patients with neuropathology, psychiatric diseases, and neurodegeneration. Clinical trials evaluating its role in treating patients are needed. CO, like nitric oxide before it, may prove a novel treatment approach to CNS disease and health.

Effect of Extrinsic Carbon Monoxide on Neuropathology

By evaluating the biochemical reactions during apoptosis in the brain, prevention of clinically defined disease may be attainable. Metabonomics is becoming more widely used in defining the presence of neuropathologies and response to treatment. Use in further refining our understanding of biochemical pathways should result in the development of better therapies and improvement in outcomes. CO therapy for neurodegenerative diseases is being evaluated and response is dependent on dosing and timing. Metabonomic profiling will provide further insight into the role of CO in CNS health and will add greatly to our ability to improve the quality of life in our patients with neurodegenerative disorders, psychiatric diseases, and neuropathologies.


  1. Patti GJ, Yanes O, Siuzdak G (2012) Innovation: metabolomics: The apogee of the omics trilogy. Nat Rev Mol Cell Biol 13(4): 263-269.
  2. Lindon JC, Holmes E, Nicholson JK (2003) So what’s the deal with metabonomics? Anal Chem 75(17): 384A-391A.
  3. Nicholson JK, Lindon JC, Holmes E (1999) ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29(11): 1181-1189.
  4. Daviss B (2005) Growing pains for metabolomics. The Scientist 19(8): 25- 28.
  5. Goeddel LC, Patti GJ (2012) Maximizing the value of metabolomic Data. Bioanalysis 4(18): 2199-2201.
  6. Mayr M (2008) Metabolomics: ready for the prime time? Circ Cardiovasc Genet 1(1): 58-65.
  7. Patti GJ, Tautenhahn R, Rinehart D, Cho K, Shriver L, et al. (2013) A view from above: cloud plots to visualize global metabolomic data. Anal Chem 85(2): 798-804.
  8. Patti GJ, Tautenhahn R, Siuzdak G (2012) Meta-analysis of untargeted metabolomic data from multiple profiling experiments. Nat Protoc 7(3): 508-516.
  9. Tautenhahn R, Cho K, Uritboonthai W, Zhu Z, Patti GJ, et al. (2012) An accelerated workflow for untargeted metabolomics using the METLIN database. Nat Biotechnol 30(9): 826-828.
  10. Rubakhin SS, Romanova EV, Nemes P, Sweedler JV (2011) Profiling metabolites and peptides in single cells. Nat Methods 8(4 Suppl): S20-S29.
  11. Rubakhin SS, Lanni EJ, Sweedler JV (2013) Progress toward single cell metabolomics. Curr Opin Biotechnol 24(1): 95-104.
  12. Bezabeh T, Ijare OB, Nikulin AE, Somorjai RL, Smith IC (2014) MRSbased metabolomics in cancer research. Magn Reson Insights 7: 1-14.
  13. Caudle WM, Bammler TK, Lin Y, Pan S, Zhang J (2010) Using ‘omics’ to define pathogenesis and biomarkers of Parkinson’s disease. Expert Rev Neurother 10(6): 925-942.
  14. Fanos V, Antonucci R, Barberini L, Noto A, Atzori L (2012) Clinical application of metabolomics in neonatology. J Matern Fetal Neonatal Med 25(Suppl 1): 104-109.
  15. Fanos V, Van den Anker J, Noto A, Mussap M, Atzori L (2013) Metabolomics in neonatology: fact or fiction? Semin Fetal Neonatal Med 18(1):3-12.
  16. Fanos V, Atzori L, Makarenko K, Melis GB, Ferrazzi E (2013) Metabolomics application in maternal-fetal medicine. Biomed Res Int 1-9.
  17. Gill SK, Wilson M, Davies NP, MacPherson L, English M, et al. (2014) Diagnosing relapse in children’s brain tumors using metabolite profiles. Neuro Oncol 16(1): 156-164.
  18. Jové M, Portero-Otín M, Naudí A, Ferrer I, Pamplona R (2014) Metabolomics of human brain aging and age-related neurodegenerative diseases. J Neuropathol Exp Neurol 73(7): 640-657.
  19. Kim E, Jung YS, Kim H, Kim JS, Park M, et al. (2014) Metabolomic signatures in peripheral blood associated with Alzheimer’s Disease amyloid-β- induced neuroinflammation. J Alzheimers Dis 42(2): 421-433.
  20. Ma D, Guest PC, Bahn S (2009) Metabonomic studies of schizophrenia and psychotropic medications: focus on alterations in CNS energy homeostasis. Bioanalysis 1(9): 1615-1626.
  21. Maletić-Savatić M, Vingara LK, Manganas LN, Li Y, Zhang S, et al. (2008) Metabolomics of neural progenitor cells: a novel approach to biomarker discovery. Cold Spring Harb Symp Quant Biol 73: 389-401.
  22. Mauri-Capdevila G, Jove M, Suarez-Luis I, Portero-Otin M, Purroy F (2013) Metabolomics in ischaemic stroke, new diagnostic and prognostic biomarkers. Rev Neurol 57(1): 29-36.
  23. Wang Y, Wang YG, Ma TF, Li M, Gu SL (2014) Dynamic metabolites profile of cerebral ischemia/reperfusion revealed by (1)H NMR-based metabolomics contributes to potential biomarkers. Int J Clin Exp Pathol 7(7):4067-4075.
  24. Chen J (2014) Hemeoxygenase in neuroprotection: from mechanisms to therapeutic implications. Rev Neurosci 25(2): 269-280.
  25. Hanafy KA, Oh J, Otterbein LE (2013) Carbon monoxide and the brain: time to rethink the dogma. Curr Pharm Des 19(15): 2771-2775.
  26. Cutajar MC, Edwards TM (2007) Evidence for the role of endogenous carbon monoxide in memory processing. J Cogn Neurosci 19(4): 557-562.
  27. Leffler CW, Parfenova H, Jaggar JH (2011) Carbon monoxide as an endogenous vascular modulator. Am J Physiol Heart Circ Physiol 301(1): H1-H11.
  28. Li A, Xi Q, Umstot ES, Bellner L, Schwartzman ML, et al. (2008) Astrocyte- derived CO is a diffusible messenger that mediates glutamate-induced cerebral arteriolar dilation by activating smooth muscle cell KCa channels.Circ Res 102(2): 234-241.
  29. Mahan VL (2012) Neuroprotective, neurotherapeutic, and neurometabolic effects of carbon monoxide. Med Gas Res 2(1): 32.
  30. Mancuso C (2004) Hemeoxygenase and its products in the nervous system. Antioxid Redox Signal 6(5): 878-887.
  31. Mancuso C, Perluigi M, Cini C, De Marco C, Giuffrida Stella AM, et al. (2006) Hemeoxygenase and cyclooxygenase in the central nervous sytem: a functional interplay. J Neurosci Res 84(7): 1385-1391.
  32. Parfenova H, Leffler CW (2008) Cerebroprotective functions of HO-2. Curr Pharm Des 14(5): 443-453.
  33. Ryter SW, Morse D, Choi AM (2004) Carbon monoxide: to boldly go where NO has gone before. Sci STKE 2004(230): RE6.
  34. Ryter SW, Otterbein LE, Morse D, Choi AM (2002) Hemeoxygenase/carbon monoxide signaling pathways: regulation and functional significace. Mol Cell Biochem234-235(1-2):249-263.
  35. Wu L, Wang R (2005) Carbon monoxide: endogenous production, physiological functions, and pharmacological applications. Pharmacol Rev 57(4): 585-630.
  36. Barañano DE, Snyder SH (2001) Neural roles for hemeoxygenase: contrasts to nitric oxide synthase. Proc Natl Acad Sci USA 98(20): 10996-11022.
  37. Barone E, Di Domenico F, Mancusco C, Butterfield DA (2014) The janus face of the hemeoxygenase/biliverdin reductase system in Alzheimer disease: it’s time for reconciliation. Neurobiol Dis 62: 144-159.
  38. Bor-Seng-Shu E, Kita WS, Figueiredo EG, Paiva WS, Fonoff ET, et al. (2012) Cerebral hemodynamics: concepts of clinical importance. Arq Neuropsiquiatr 70(5): 352-356.
  39. Fujita K, Yamafuji M, Nakabeppu Y, Noda M (2012) Therapeutic approach to neurodegenerative diseases by medical gases: focusing on redox signaling and related antioxidant enzymes. Oxid Med Cell Longev 2012: 324256.
  40. Kajimura M, Fukuda R, Bateman RM, Yamamoto T, Suematsu M (2010) Interactions of multiple gas-transducing systems: hallmarks and uncertainties of CO, NO, and H2S gas biology. Antioxid Redox Signal 13(2): 157-192.
  41. Li L, Moore PK (2007) An overview of the biological significance of endogenous gases: new roles for old molecules. Biochem Soc Trans 35(Pt 5):1138-1141.
  42. Liu Y, Li Z, Shi X, Liu Y, Li W, et al. (2014) Neuroprotection of upregulated carbon monoxide by electrical acupuncture on perinatal hypoxicischemic brain damage in rats. Neurochem Res 39(9):1724-1732.
  43. Mancuso C, Navarra P, Preziosi P (2010) Roles of nitric oxide, carbon monoxide, and hydrogen sulfide in the regulation of the hypothalamic-pituitaryadrenal axis. J Neurochem 113(3): 563-575.
  44. Queiroga CS, Vercelli A,Vieira HL (2015) Carbon monoxide and the CNS: challenges and achievements. Br J Pharmacol 172(6): 1533-1545.
  45. Schallner N, Romão CC, Biermann J, Lagrèze WA, Otterbein LE, et al. (2013) Carbon monoxide abrogates ischemic insult to neuronal cells via the soluble guanylate cyclase-cGMP pathway. PLoS One 8(4): e60672.
  46. Sharp FR, Zhan X, Liu DZ (2013) Heat shock proteins in the brain: role of Hsp70, Hsp27, and HO-1 (Hsp32) and their therapeutic potential. Transl Stroke Res 4(6): 685-692.
  47. Jackson AA (2000) Nutrients, growth, and the development of programmed metabolic function. Adv Exp Med Biol 478: 41-55.
  48. Kolb B, Mychasiuk R, Williams P, Gibb R (2011) Brain plasticity and recovery from early cortical injury. Dev Med Child Neurol 53(Suppl 4): 4-8.
  49. Levitt P, Reinoso B, Jones L (1998) The critical impact of early cellular environment on neuronal development. Prev Med 27(2): 180-183.
  50. Naruse I, Keino H (1995) Apoptosis in the developing CNS. Prog Neurobiol 47(2): 135-155.
  51. Sarnat HB, Flores-Sarnat L (2013) Neuroembryology and brain malformations: an overview. Handb Clin Neurol 111: 117-128.
  52. Simerly RB (2008) Hypothalamic substrates of metabolic imprinting. Physiol Behav 94(1): 79-89.
  53. Abdelmoula WM, Carreira RJ, Shyti R, Balluff B, van Zeijl RJ, et al. (2014) Automatic registration of mass spectrometry imaging data sets to the Allen Brain Atlas. Anal Chem 86(8): 3947-3954.
  54. Hu Y, Li J, Yan W, Chen J, Li Y, et al. (2013) Identifying novel glioma associated pathways based on systems biology level meta-analysis. BMC Syst Biol 7(Suppl 2): S9.
  55. Lalande J, Halley H, Balayssac S, Gilard V, Dèjean S, et al. (2014) 1H NMR metabolomic signatures in five brain regions of the AßPPswe Tg2576 mouse model of Alzheimer’s disease at four ages. J Alzheimers Dis 39(1): 121-143.
  56. Locasalle JW, Melman T, Song S, Yang X, Swanson KD, et al. (2012) Metabolomics of human cerebrospinal fluid identifies signatures of malignant glioma. Mol Cell Proteomics 11(6): M111-014688.
  57. Monleón D, Morales JM, Gonzalez-Darder J, Talamantes F, Cortés O, et al. (2008) Benign and atypical meningioma metabolic signatures by highresolution magic-angle spinning molecular profiling. J Proteome Res 7(7):2882-2888.
  58. Petrik V, Loosemore A, Howe FA, Bell BA, Papadopoulos MC (2006) OMICS and brain tumour biomarkers. Br J Neurosurg 20(5): 275-280.
  59. Prabakaran S, Swatton JE, Ryan MM, Huffaker SJ, Huang JT, et al. (2004) Mitochondrial dysfunction in schizophrenia: evidence for compromised brain metabolism and oxidative stress. Mol Psychiatry 9(7): 684-697.
  60. Trushina E, Nemutlu E, Zhang S, Christensen T, Camp J, et al. (2012) Defects in mitochondrial dynamics and metabolomic signatures of evolving energetic stress in mouse models of familial Alzheimer’s disease. PLoS One 7(2): e32737.
  61. Vingara LK, Yu HJ, Wagshul ME, Serafin D, Christodoulou C, et al. (2013) Metabolomic approach to human brain spectroscopy identifies associations between clinical features and the frontal lobe metabolome in multiple sclerosis. Neuroimage 82: 586-594.
  62. Carter AR, Shulman GL, Corbetta M (2012) Why use a connectivity-based approach to study stroke and recovery of function? Neuroimage 62(4): 2271-2280.
  63. Corbetta M (2012) Functional connectivity and neurological recovery. Dev Psychobiol 54(3): 239-253.
  64. Damoiseaux JS, Greicius MD (2009) Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity. Brain Struct Funct 213(6): 525-533.
  65. Deco G, Corbetta M (2011) The dynamic balance of the brain at rest. Neuroscientist 17(1): 107-123.
  66. Gillebert CR, Mantini D (2013) Functional connectivity in the normal and injured brain. Neuroscientist 19(5): 590-522.
  67. Grefkes C, Fink GR (2011) Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches. Brain 134(Pt 5): 1264-1276.
  68. He BJ, Shulman GL, Snyder AZ, Corbetta M (2007) The role of impaired neuronal communication in neurological disorders. Curr Opin Neurol 20(6): 655-660.
  69. Jiang L, Xu H, Yu C (2013) Brain connectivity plasticity in the motor network after ischemic stroke. Neural Plast 2013: 924192.
  70. Rehme AK, Grefkes C (2013) Cerebral network disorders after stroke: evidence from imaging-based connectivity analyses of active and resting brain states in humans. J Physiol 591(Pt 1): 17-31.
  71. Aswendt M, Adamczak J, Tennstaedt A (2014) A review of novel optical imaging strategies of the stroke pathology and stem cell therapy in stroke. Front Cell Neurosci 8: 226.
  72. Belanger HG, Vanderploeg RD, Curtiss G, Warden DL (2007) Recent neuroimaging techniques in mild traumatic brain injury. J Neuropsychiatry Clin Neurosci 19(1): 5-20.
  73. Bonaffini N, Altieri M, Rocco A, Di Piero V (2002) Functional neuroimaging in acute stroke. Clin Exp Hypertens 24(7-8): 647-657.
  74. Byrnes KR, Wilson CM, Brabazon F, von Leden R, Jergens JS, et al. (2014) FDG-PET imaging in mild traumatic brain injury: a critical review. Front Neuroenergetics 5:13.
  75. Filippi M, Charil A, Rovaris M, Absinta M, Rocca MA (2014) Insights from magnetic resonance imaging. HandbClinNeurol122:115-149.
  76. Nour M, Liebeskind DS (2014) Imaging of cerebral ischemia: from acute stroke to chronic disorders. Neurol Clin 32(1): 193-209.
  77. Plantier D, Bussy E, Rimbot A, Maszelin P, Tournebise H (2006) Neuroradiological investigations in mild brain injuries: state of the art and practical recommendations. Rev Stomatol Chir Maxillofac 107(4): 218-232.
  78. Shenton ME, Hamoda HM, Schneiderman JS, Bouix S, Pasternak O, et al. (2012) A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury. Brain Imaging Behav 6(2):137-192.
  79. Stevens RD, Sutter R (2013) Prognosis in severe brain injury. Crit Care Med 41(4): 1104-1123.
  80. Gavaghan CL, Holmes E, Lenz E, Wilson ID, Nicholson JK (2000) An NMR-based metabonomic approach to investigate the biochemical consequences of genetic strain differences: application to the C57BL10J and Alpk:ApfCD mouse. FEBS Lett 484(3): 169-174.
  81. Zhang GF, Sadhukhan S, Tochtrop GP, Brunengraber H (2011) Metabolomics, pathway regulation, and pathway discovery. J Biol Chem 286(27): 23631-23635.
  82. Cho WC (2007) Integrated therapy and research progress in molecular therapy for intracranial tumor. Nan Fang Yi Ke Da Xue Xue Bao 27(7): 1047-1051.
  83. Glenn TC, Hirt D, Mendez G, McArthur DL, Sturtevant R, et al. (2013) Metabolomic analysis of cerebral spinal fluid from patients with severe brain injury. Acta Neurochir Suppl 118: 115-119.
  84. Hassan-Smith G, Wallace GR, Douglas MR, Sinclair AJ (2012) The role of metabolomics in neurological disease. J Neuroimmunol 248(1-2): 48-52.
  85. Ibrahim SM, Gold R (2005) Genomics, proteomics, metabolomics: what is in a word for multiple sclerosis? Curr Opin Neurol 18(3): 231-235.
  86. Kaddurah-Daouk R, Krishnan KR (2009) Metabolomics: a global biochemical approach to the study of central nervous system diseases. Neuropsychopharmacology 34(1): 173-186.
  87. Keller M, Enot DP, Hodson MP, Igwe El, Deigner HP, et al. (2011) Inflammatory- induced hibernation in the fetus: priming of fetal sheep metabolism correlates with developmental brain injury. PLoS One 6(12): e29503.
  88. Levine J, Panchalingam K, McClure RJ2, Gershon S, Pettegrew JW (2000) Stability of CSF metabolites measured by proton NMR. J Neural Transm 107(7): 843-848.
  89. Quinones MP, Kaddurah-Daouk R (2009) Metabolomics tools for identifying biomarkers for neuropsychiatric diseases. Neurobiol Dis 35(2): 165-176.
  90. Ramin SL, Tognola WA, Spotti AR (2003) Proton magnetic resonance spectroscopy: clinical applications in patients with brain lesions. Sao Paulo Med J 121(6): 254-259.
  91. Ross B, Tran T, Bhattacharya P, Watterson DM, Sailasuta N (2011) Application of NMR spectroscopy in medicinal chemistry and drug discovery. Curr Top Med Chem 11(1): 93-114.
  92. Sanz-Cortés M, Carbajo RJ, Crispi F, Figueras F, Pineda-Lucena A, et al. (2013) Metabolomic profile of umbilical cord blood plasma from early and late intrauterine growth restricted (IUGR) neonates with and without signs of brain vasodilation. PLoS One 8(12): e80121.
  93. Viant MR, Lyeth BG, Miller MG, Berman RF (2005) An NMR metabolomic investigation of early metabolic disturbances following traumatic brain injury in a mammalian model. NMR Biomed 18(8): 507-516.
  94. Zhang A, Sun H, Wang P, Han Y, Wang X (2012) Recent and potential developments of biofluid analyses in metabolomics. J Proteomics 75(4): 1079-1088.
  95. Goodacre R, Vaidyanathan S, Dunn WB, Harrigan GG, Kell DB (2004) Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol 22(5): 245-252.
  96. Skappak C, Regush S, Cheung PY, Adamko DJ (2013) Identifying hypoxia in a newborn piglet model using urinary NMR metabolomic profiling. PLoS One 8(5): e65035.
  97. Solberg R, Enot D, Deigner HP, Koal T, Scholl-Bürgi S, et al. (2010) Metabolomic analyses of plasma reveals new insights into asphyxia and resuscitation in pigs. PLoS One 5(3): e9606.
  98. van Cappellen van Walsum AM, Jongsma HW, Wevers RA, Nijhuis JG, Crevels J, et al. (2001) Hypoxia in fetal lambs: a study with (1)H-NMR spectroscopy of cerebrospinal fluid. Pediatr Res 49(5): 698-704.
  99. Beckstrom AC, Humston EM, Snyder LR, Synovec RE, Juul SE (2011) Application of comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry method to identify potentical biomarkers of perinatal asphyxia in a non-human primate model. J Chromatogr A 1218(14): 1899-1906.
  100. Sinclair AJ, Viant MR, Ball AK, Burdon MA, Walker EA, et al. (2010) NMR-based metabolomic analysis of cerebrospinal fluid and serum in neurological diseases – a diagnositic tool? NMR Biomed 23(2): 123-132.
  101. Huang CC, Wang ST, Chang YC, Lin KP, Wu PL (1999) Measurement of the urinary lactate: creatinine ratio for the early identification of newborn infants at risk for hypoxic-ischemic encephalopathy. N Engl J Med 341(5):328-335.
  102. Reinke SN, Walsh BH, Boylan GB, Sykes BD, Kenny LC, et al. (2013) 1H NMR derived metobolomic profile of neonatal asphyxia in umbilical cord serum: implications for hypoxic ischemic encephalopathy. J Proteome Res 12(9): 4230-4239.
  103. Davies SK, Ang JE, Revell VL, Holmes B, Mann A, et al. (2014) Effect of sleep deprivation on the human metabolome. Proc Natl Acad Sci USA 111(29): 10761-10766.
  104. Galluzzi L, Bravo-San Pedro JM, Vitale I, Aaronson SA, Abrams JM, et al. (2015) Essential versus accessory aspects of cell death: recommendations of the NCCD 2015. Cell Death Differ 22(1): 58-73.
  105. Galluzzi L, Vitale I, Abrams JM, Alnemri ES, Baehrecke EH, et al. (2012) Molecular definitions of cell death subroutines: recommendations of the nomenclature committee on cell death 2012. Cell Death Differ 19(1): 107-120.
  106. Cho K, Searle K, Webb M, Yi H, Ferreira PA (2012) Ranbp2 haploinsufficiency mediates distinct cellular and biochemical phenotypes in brain and retinal dopaminergic and glia cells elicited by the parkinsonian neurotoxin, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Cell Mol Life Sci 69(20): 3511-3527.
  107. Tsang TM, Haselden JN, Holmes E (2009) Metabonomic characterization of the 3-Nitropropionic acid rat model of Huntington’s disease. Neurochem Res 34(7): 1261-1271.
  108. Guan Q, Liang S, Wang Z, Yang Y, Wang S (2014) 1H NMR-based metabonomic analysis of the effect of optimized rhubarb aglycone on the plasma and urine metabolic fingerprints of focal cerebral ischemia-reperfusion rats. J Ethnopharmacol 154(1): 65-75.
  109. Wegiel B, Nemeth Z, Correa-Costa M, Bulmer AC, Otterbein LE (2014) Heme oxygenase-1: a metabolic nike. Antioxid Redox Signal 20(11): 1709-1722.
  110. Chan MK (2001) Recent advances in heme-protein sensors. Curr Opin Chem Biol 5(2): 216-222.
  111. Helbing J, Devereux M, Nienhaus K, Nienhaus GU, Hamm P, et al. (2012) Temperature dependence of the heat diffusivity of proteins. J Phys Chem A 116(11): 2620-2628.
  112. Jain R, Chank MK (2003) Mechanisms of ligand discrimination by heme proteins. J Biol Inorg Chem 8(1-2): 1-11.
  113. Nuernberger P, Lee KF, Bonvalet A, Bouzhir-Sima L, Lambry JC, et al.(2011) Strong ligand-protein interactions revealed by ultrafast infrared spectroscopy of CO in the heme pocket of the oxygen sensor FixL. J Am Chem Soc 133(43): 17110-17113.
  114. Pietra F (2012) On CO egress from, and re-uptake by, the enzyme MauG, as a mimic of the acquisition of oxidizing agents by the pre-MADH_MauG system. A molecular mechanics approach. Chem Biodivers 9(8): 1425-1435.
  115. Rahman MN, Vlahakis JZ, Vukomanovic D, Lee W, Szarek WA, et al. (2012) A novel, “double-clamp” binding mode for human heme oxygenase- 1 inhibition. PLoS One 7(1): e29514.
  116. Hishiki T, Yamamoto T, Morikawa T, Kubo A, Kajimura M, et al. (2012) Carbon monoxide: impact on remethylation/transsulfuration metabolism and its pathophysiologic implications. J Mol Med 90(3): 245-254.
  117. Wu L, Wang R (2005) Carbon monoxide: endogenous production, physiological functions, and pharmacological applications. Pharmacol Rev 57(4): 585-630.
  118. Otterbein LE, Choi AMK (2000) Heme oxygenase: colors of defense against cellular stress. Am J Physiol Lung Cell Mol Physiol 279(6): L1029-L1037.
  119. Otterbein LE, Soares MP, Yamashita K, Bach FH (2003) Heme oxygenase- 1: unleashing the protective properties of heme. Trends Immunol 24(8): 449-455.
  120. Marilena G (1997) New physiological importance of two classic residual products: carbon monoxide and bilirubin. Biochem Mol Med 61(2): 136- 142.
  121. Dennery PA (2014) Signaling function of hemeoxygenase proteins. Antioxid Redox Signal 20(11): 1743-1753.
  122. Dwyer BE, Nishimura RN, Lu SY (1995) Differential expression of heme oxygenase-1 in cultured cortical neurons and astrocytes determined by the aid of a new hemeoxygenase antibody. Response to oxidative stress. Brain Res Mol Brain Res 30(1): 37-47.
  123. Parfenova H, Leffler CW, Basuroy S, Liu J, Fedinec AL (2012) Antioxidant roles of hemeoxygenase, carbon monoxide, and bilirubin in cerebral circulation during seizures. J Cereb Blood Flow Metab 32(6): 1024-1034.
  124. Hung SY, Liou HC, Kang KH, Wu RM, Wen CC, et al. (2008) Overexpression of heme oxygenase-1 protects dopaminergic neurons against 1-methyl- 4-phenylpyridinium-induced neurotoxicity. Mol Pharmacol 74(6): 1564-1575.
  125. Doré S, Goto S, Sampei K, Blackshaw S, Hester LD, et al. (2000) Heme oxygenase-2 acts to prevent neuronal death in brain cultures and following transient cerebral ischemia. Neuroscience 99(4): 587-592.
  126. Jazwa A, Cuadrado A (2010) Targeting heme oxygenase-1 for neuroprotection and neuroinflammation in neurodegenerative diseases. Curr Drug Targets 11(12): 1517-1531.
  127. Kensler TW, Wakabayashi N, Biswal S (2007) Cell survival responses to environmental stresses via the Keap1-Nrf2-ARE pathway. Annu Rev Pharmacol Toxicol 47: 89-116.
  128. Ambegaokar SS, Kolson DL (2014) Heme oxygenase-1 dysregulation in the brain: implications for HIV-associated neurocognitive disorders. Curr HIV Res 12(3): 174-188.
  129. Barger SW, Fiscus RR, Ruth P, Hofmann F, Mattson MP (1995) Role of cyclic GMP in the regulation of neuronal calcium and survival by secreted forms of β-amyloid precursor. J Neurochem 64(5): 2087-2096.
  130. Benvenisti-Zarom L, Regan RF (2007) Astrocyte-specific heme oxygenase-1 hyperexpression attenuates heme-mediated oxidative injury. Neurobiol Dis 26(3): 688-695.
  131. Frandsen A, Andersen CF, Schousboe A (1992) Possible role of cGMP in excitatory amino acid induced cytotoxicity in cultured cerebral cortical neurons. Neurochem Res 17(1): 35-43.
  132. Garthwaite G, Garthwaite J (1988) Cyclic GMP and cell death in rat cerebellar slices. Neuroscience 26(1): 321-326.
  133. Min KJ, Yang MS, Kim SU, Jou I, Joe EH (2006) Astrocytes induce heme oxygenase-1 expression in microglia: a feasible mechanism for preventing excessive brain inflammation. J Neurosci 26(6): 1880-1887.
  134. Schipper HM (2004) Heme oxygenase-1: transducer of pathological brain iron sequestration under oxidative stress. Ann NY Acad Sci 1012: 84-93.
  135. Zhang K, Kaufman RJ (2006) The unfolded protein response: a stress signaling pathway critical for health and disease. Neurology 66(2 Suppl 1): S102-109.
  136. Calabrese V, Cornelius C, Mancuso C, Pennisi G, Calafato S, et al. (2008) Cellular stress response: a novel target for chemoprevention and nutritional neuroprotection in aging, neurodegenerative disorders and longevity. Neurochem Res 33(12): 2444-2471.
  137. Maines MD (1997) The hemeoxygenase system: a regulator of second messenger gases. Annu Rev Pharmacol Toxicol 37: 517-554.
  138. Mancuso C, Pistritto G, Tringali G, Grossman AB, Preziosi P, et al. (1997) Evidence that carbon monoxide stimulates prostaglandin endoperoxide synthase activity in rat hypothalamic explants and in primary cultures of rat hypothalamic astrocytes. Brain Res Mol Brain Res 45(2): 294-300.
  139. Jaggar JH, Leffler CW, Cheranov SY, Tcheranova D, Cheng X, et al. (2002) Carbon monoxide dilates cerebral arterioles by enhancing the coupling of Ca2+ sparks to Ca2+ -activated K+ channels. Circ Res 91(7): 610-617.
  140. Otterbein LE, Bach FH, Alam J, Soares M, Tao Lu H, et al. (2000) Carbon monoxide has anti-inflammatory effects involving the mitogen-activated protein kinase pathway. Nat Med 6(4): 422-428.
  141. Sutherland BA, Harrison JC, Nair SM, Sammut IA (2013) Inhalation Gases or gaseous mediators as neuroprotectants for cerebral ischaemia. Curr Drug Targets 14(1): 56-73.
  142. Yabluchanskiy A, Sawle P, Homer-Vanniasinkam S, Green CJ, Foresti R, et al. (2012) CORM-3, a carbon monoxide-releasing molecule, alters the inflammatory response and reduces brain damage in a rat model of hemorrhagic stroke. Crit Care Med 40(2): 544-552.
  143. Wang B, Cao W, Biswal S, Doré S (2011) Carbon monoxide-activated Nrf2 pathway leads to protection against permanent focal cerebral ischemia. Stroke 42(9): 2605-2610.
  144. Mahan VL, Zurakowski D, Otterbein LE, Pigula FA (2012) Inhaled carbon monoxide provides cerebral cytoprotection in pigs. PLoS One 7(8): e41982.
  145. Zeynalov E, Doré S (2009) Low doses of carbon monoxide protect against experimental focal brain ischemia. Neurotox Res 15(2): 133-137.

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