Inhalation of pollutants and the impact on the CNS
Diesel exhaust (DE) largely consists of diesel exhaust nanoparticles (DENPs) (1). DE forms an important contribution to the total particulate matter in air pollution, and therefore poses a major threat for human health (8). In the past, human exposure to DE has been widely studied, but mostly in relation to pulmonary or cardiovascular diseases (2,4). In recent years, the possible effects of NPs on the central nervous system (CNS) have been highlighted, and observed changes in brain activity are hypothesized to be caused by NPs affecting signaling in the CNS (3). Deposition of NPs in the brain leads to neuroinflammation, a precursor of neurodegenerative disorders such as Alzheimer’s disease (AD) (3).
Systemic transport of pollutants to the CNS is limited by the blood brain barrier (BBB). NP inhalation and transport through the olfactory pathway bypasses the BBB and minimizes systemic exposure (21). Furthermore, people who have been exposed for a prolonged time to high concentrations of air pollution frequently experience damage to the olfactory mucosa, the olfactory bulb and the prefrontal cortex. Olfactory dysfunctions are detected in approximately 90% of AD patients (9). Despite this correlation is the molecular mechanisms behind air pollution-induced development of AD still poorly understood. However, new evidence suggests the role of NPs in activating microglial cells, causing neuroinflammation and oxidative stress (22). Additionally, NPs are also absorbed through the respiratory system and transported to the systemic circulation. This can lead to respiratory and systemic inflammation, resulting in an increase of pro-inflammatory cytokines. Both NPs and cytokines can be actively transported through the BBB (23). Pro-inflammatory cytokines are associated with microglial activation, but this will be discussed later.
Alzheimer’s disease: Oxidative damage and plaque forming
AD is an irreversible neurodegenerative disorder, affecting millions of people worldwide. Many risk factors have been highlighted over the years, with advanced age and family history as the most proven factors (9). The exact mechanism behind neuronal degeneration is yet to be fully understood, but increasing evidence indicates oxidative stress as important mediator. Several studies showed AD patients with brain damage as a result of oxidative stress, associated with increased products of protein oxidation and lipid peroxidation. This is the result of a state of redox imbalance due to an increased production of reactive oxygen species (ROS). ROS are a group of oxygen containing molecules with a high reactive potential. They are products of regular cell processes, such as the generation of ATP in the mitochondria, and maintained by antioxidant enzymes or compounds which remove or repair oxidized molecules (10). When the antioxidant defense mechanism is unable to maintain the increased ROS levels, oxidative damage will occur. Accumulation of ROS can damage lipid proteins or nucleic acids in the cell when a toxic threshold is reached (9).
The brain is especially vulnerable to oxidative stress, due to its high concentration of lipids, physiological oxygen and high energy use. In AD patients, oxidative stress eventually leads to reduced motor skills and cognitive abilities (12). One important physiological symptom of AD is the emergence of extracellular compact fibrils, or senile plaques, surrounding neurons (13). Oxidized macromolecules, products of oxidative stress, have been identified in these plaques (10). Most abundant components of these plaques are the amyloid-β (Aβ) peptides, which when accumulated are characteristic for AD. A proteolytic cleavage of amyloid precursor protein (APP) occurs physiological or pathological (non-amyloidogenic and amyloidogenic, respectively). Only the amyloidogenic route produces Aβ peptides; APP is cleaved by β- and ɣ-secretases, generating many different Aβ peptides, all 37-43 amino acids long (11). Most abundant form of Aβ peptides under normal conditions is 40 amino acids long (Aβ40). However, in AD patients, Aβ42 is the most abundant form and susceptible to aggregate. The cleavage site of ɣ-secretases determines whether Aβ40 or Aβ42 is formed (19). Monomers of Aβ42 can form many different structures like oligomers, fibrils and the AD characteristic senile plaques. Which structures are formed depends on the Aβ coding region of APP and post-translational modifications (20) . In figure 1, the thickness of the arrow indicates the probability of aggregation of corresponding Aβ42 polymers (19). Formation of the plaques is most likely according to this figure.
Figure 1: Generation of Aβ peptides by cleaving APP and the following extracellular formation of varied Aβ peptide aggregations. Probability of aggregation is represented by the thickness of the arrows. (19)
Plaques emerge when Aβ peptides accumulate and polymerisation occurs. However, it is still under discussion whether this deposition of Aβ peptides in the CNS is the cause or result of oxidative stress (9). A possible function of this deposition is shielding neurons against oxidative stress. However, in a later stage of AD progression, Aβ peptides act no longer as a defence mechanism but rather as an important mediator of AD progression (14). Instead of defending neurons against oxidative damage, plaques become pro-oxidants and induce oxidative stress which causes the neurons to eventually degenerate (9). Additionally, microglial cells are known to assemble around plaques where they are activated and induce a neuroinflammatory response (16) . Neuroinflammation is a regular immune response. However, it can induce oxidative stress and lead to neurodegeneration if not properly controlled.
Neuroinflammation: Activation of microglial cells
Microglial cells in the CNS act as components of the immune system in the CNS, analogous to macrophages and lymphocytes in the peripheral nervous system. In absence of inflammation are microglial cells in a non-active resting phase, scanning for pathogens and damaged tissue. A large variety of receptors are used to recognize pathogen and damage associated patterns (PAMP and DAMP respectively) (16). Receptors for advanced glycation products (RAGE) are identified as activators of microglial cells after binding Aβ peptide aggregations. Additionally, increased expression of RAGE in the CNS of AD patients suggests Aβ peptide accumulation to induce neuroinflammation by activating microglial cells (17).
Recognition of DAMPs or PAMPs leads to an acute activation of microglial cells. Activation triggers a chain of reactions resulting in an increased expression of immune response related genes, such as major histocompatibility complex (MHC) proteins, and inducing a state of chronic neuroinflammation. Maintaining a chronic state of neuroinflammation is associated with the continuous presence of DAMPs and/or PAMPs in combination with increased expression of pro-inflammatory cytokines. Pro-inflammatory cytokines, including tumor necrosis factor α (TNFα), nitric oxide (NO) and interleukin-1β (IL-1β) and IL-6, induce activation of other microglial cells and therefore sustaining a chronic neuroinflammation. Furthermore, activation of microglial cells coincides with elevated production of ROS and thus inducing oxidative stress (16).
Activation of microglial cells are essential in the early stages of AD development. Interestingly, microglial cells are activated by DENPs in the CNS (18). Binding of DENPs to the macrophage-1 (MAC1) receptor activates microglial cells and induces the production of ROS. The MAC1 receptor, previously described as lipopolysaccharide receptor in phagocytes (Pereraet al. 1997), is expressed on the surface of microglial cells and recognizes DENPs as PAMPs. However, It is unclear how this receptor-ligand binding is established (18). Nevertheless, exposure to DE and accumulation of DENPs in the CNS results in an activation of microglial cells due to PAMP recognition. Continuous presence of DENPs and absence of pathogens leads to a chronic state of neuroinflammation.
Described here is a chain of reactions involving exposure to air-pollution, onset and maintenance of microglia-induced chronic neuroinflammation and accumulation of ROS in the CNS, eventually resulting in neurodegeneration. Additionally, there is a strong association between oxidative stress and the accumulation and aggregation of Aβ peptides. It is unclear whether Aβ peptides cause or result from oxidative stress, but interestingly they are recognized by RAGE and activate microglial cells. Pro-inflammatory cytokines maintain a state of chronic inflammation and are, as well as Aβ peptides, indicators of neurodegenerative progression. In figure 2, an overview is given containing a simplified view of the onset and maintenance of neuroinflammation as discussed above.
Figure 2: Schematic view of the activation of microglial cells, maintenance of this activation and resulting neurodegeneration. Link between Aβ peptides and oxidative stress due to ROS production remains unclear. Increased proinflammatory cytokine production by active microglial cells leads to additional activation of other microglial cells. (19)
Air pollution is often associated with increased risk of respiratory, cardiovascular and cerebrovascular disease. Assessing the neurological cognitive functioning and impairment as a result of realistic exposure levels in humans remains difficult to study. Mouse models can be used to measure cognitive decline after long term chronic exposure. (uitbereiden met opzet mouse model)
Characteristics such as particle size and chemical composition can have a causal relationship with the immune effects caused. The location of accumulation and what cell type clears the xenobiotic are size dependent. Particle structure is linked to the potency to induce ROS (bron&link Berend). The adverse effect caused by ambient particles is also strongly affected by environmental factors. Genetic predisposition, microbial components, eating and drinking habits adjust the local impact and biotransformation potential. All of these environmental and genetic factors influence the risk assessment for long-term damage to oxygen dependent tissue (bron).
Children are more susceptible to harm due to incomplete development of the lungs which makes it more permeable. Individuals that are outside more regularly and with a faster metabolic rate due to for example strenuous exercise are also at a higher risk. Elderly are generally less mobile, but the fragility of their lungs due to weariness also increases their risk for xenobiotic accumulation and inflammation. People with chronic respiratory diseases such as asthma which often arises from allergies find themselves more susceptible as well.
A combination of test and monitoring stations are created and distributed. Monitoring of air quality, humidity and temperature (°C) are crucial to assess pollution risk in these areas. The pollutants studied are a combination of the following: non-industrial combustion plants, road transports and traffic-related pollutants. The air pollutants considered are classified as PM10 (µg/m3). The exposure levels are classified as low: <15.4 µg/m3 , moderate: 15.4-24.8 µg/m3 and high: >24.8 µg/m3 (bron http://www.sciencedirect.com.proxy.library.uu.nl/science/article/pii/S0013935116300834 ) and are adjusted to match the average exposure in the environment if known.
Air pollution episodes are marked by the increase of emission from baseline levels. This happens when weather conditions favour the build up of pollution in the air masses. This is strongly influenced by the traffic activity.
(high, intermediate, low exposure)
One of the most polluted regions, as mentioned in the EEA (bron http://www.eea.europa.eu/publications/air-quality-in-europe-2014 ) is the A1-Saint-Denis in France. This will be a location associated with ‘high’ pollution levels since it far exceeds the 80 µg/m3 pollution limit associated with ‘alert’ threshold in France.
France was not the only European country affected by this event. Highly elevated PM concentrations were observed in the southern United Kingdom, Belgium, the Netherlands and Germany. The factors leading to such high concentration levels were a combination of various emissions sources and meteorological conditions. These weather conditions included a stable and calm weather, which prevents air pollution from dispersing; and relatively high temperatures during the daytime for the period (Bron EEA).
Figure X Waar (behalve de A1 Noord van Parijs) zetten we onze opstelling neer? Ik dacht aan high-intermediate-low exposure (dus snelweg, woongebied, landbouwgebied) en dan nog op 3 plaatsen; Parijs, België en Nederland ofzo?
Poisson regression model (to do)
The Poisson regression model is used to
“A property of the Poisson regression model is mean-variance equality, conditional on explanatory variables. ‘Regression-based’ tests for this property are proposed in a very general setting. Unlike classical statistical tests, these tests require specification of only the mean-variance relationship under the alternative, rather than the complete distribution whose choice is usually arbitrary. The optimal regression-based test is easily computed as the t-test from an auxiliary regression. If a distribution under the alternative hypothesis is in fact specified and is in the Katz system of distributions or is Cox’s local approximation to the Poisson, the score test for the Poisson distribution is equivalent to the optimal regression-based test.”
Season adaptation: Time-stratified approach
When working with statistical surveys using spread data in separate seasons it is advantageous to sample different subpopulations. The seasonal difference might influence the outcome of factors such as the air particle density. Stratification of the data collectors into homogenous subgroups before sampling creates a time-stratified approach. This approach could avoid subtle selection bias.The time-stratified approach is mutually exclusive, every element must be assigned to one stratum only. The time stratified approach is also collectively exhaustive meaning it has subgroup include all elements. When sampling systematically within each stratum the result is an improvement of representativeness and a reduced sample error.
(Crouse et al., 2009)
Human pollution induced regression examples
Multiple examples can be given of cognitive decline in human adults across a variety of air pollution exposure levels within and outside of the municipality borders. One study including adults of 55 years and older showed that a 10 µg/m3 increase in PM10 was associated with increased incident rate ratio of errors that were used to test the working memory and general orientation (Ailshire and Clarke, 2015). Another study showed a significantly reduced episodic memory ability for individuals that were exposed to the highest 25% (equivalent to 13.8-20.7 µg/m3 ) relative to the ones in the lowest 25% (equivalent to 4.5-9.9 µg/m3 ) (Ailshire and Crimmins, 2014). An aging study in Germany showed that individuals who lived near a busy road, thus with high traffic pollution exposure, were associated with a decline in olfactory and executive function (Ranft et al., 2009). The largest prospective study of 2010 of 16,000 randomly selected Chinese elderly showed a nationally representative sample associating the highest pollution levels with a 9% increase in odds for cognitive decline (Zeng et al, 2010).
Timetable of the project
Year 1 – Creating rab cages including non-filtered air pump, filtered air pump. Including automization of food and water supply and monitoring sensors of PM10, temperature and humidity.
– Pinpointing locations and studying the weather influence on pollution levels
– Maturation of rat population, health check random group selection :
Group1: short exposure (SE) low/intermediate/high : SEL, SEI, SEH
Group2: long exposure (LE) low/intermediate/high : LEL, LEI, LEH
Group3: control (CO)
– End year 1/start year 2: Start exposure experiment
Year 2 – End year 1/start year 2: Start exposure experiment
– Morris water maze cognitive functioning test of control exposure
– Euthanization, dissection and pathological examination of control exposure
– Monitor functionality of both experimental setups;
– Collect pollution rate data: first year data
– End year 2/start year 3: Remove ‘short exposure’ subpopulation
Year 3 – End year 2/start year 3: Remove ‘short exposure’ subpopulation
– Morris water maze cognitive functioning test of short exposure
– Euthanization, dissection and pathological examination of short exposure
– Monitor functionality of experiment setup ‘LE: (L/I/H)’
– Collect pollution rate data: second year data
– End year 3/start year 4: Remove ‘long exposure’ subpopulation
Year 4 – End year 3/start year 4: Remove ‘long exposure’ subpopulation
– Morris water maze cognitive functioning test of long exposure
– Euthanization, dissection and pathological examination of long exposure
– Analysation of exposure data, control, SE and LE data. Comparison of pathological data and using statistical methods for reporting results.
+ Biggeri et al., 2005
+ Yang et al., 2015
Alzheimer’s disease is a neurodegenerative disease, which is commonly known for its memory loss and decrease in spatial orientation. All rats which are used in this study are subjected to behaviour tests. It is made possible to see if the rats show a decrease in spatial memory after the exposure of DEP’s by testing the rats before and after the exposure. These tests are executed using the Morris Water Maze. (Morris, 1984) The swimming pool has a diameter of 2,14 meter and the height of 0,4 meter. It is filled with water of 26 ◦C (± 1) with the depth of 0,25 meter. On a fixed spot a hidden platform is placed. The rat will also be placed at a fixed position where it is observed to see what time it takes the rat to find the hidden platform. Then, the test is executed repeatedly (hoe vaak is genoeg?). If the rat finds the platform quicker after repeated testing, it shows that the rat has learned. After exposure (muizen doen de test niet voor én na) to DEP’s, the test is executed once again. A longer duration is an indication for neurodegeneration. (Morris, 1984) It is expected that rats who are exposed to higher concentrations of DEP are taking longer to find the platform (nog te algemeen).
Morris, R. (1984). Developments of a water-maze procedure for studying spatial learning in the rat. Journal of neuroscience methods, 11(1), 47-60
1 Kittelson, D.B. (1998) Engine and nanoparticles: A review. J. Aerosol Sci. 29 (5), 575- 588.
2 Hesterberg, T. W., Long, C. M., Sax, S. N., Lapin, C. A., McClellan, R. O., Bunn, W. B., Valberg, P. A. (2011) Particulate Matter in New Technology Diesel Exhaust (NTDE) is Quantitatively and Qualitatively Very Different from that Found in Traditional Diesel Exhaust (TDE). Journal of the Air & Waste Management Association. 61 (9), 894-913.
3 Crüts B., van Etten L., Törnqvist H., Blomberg A., Sandström T., Mills N. L., Borm PJA. 2008. Exposure to diesel exhaust induces changes in EEG in human volunteers. Part Fibre Toxicol 5:4.
4 Campen, M.J., Lund, A.K., Knuckles, T.L., Conklin, D.J., Bishop, B., Young, D., Seilkop, S., Seagrave, J., Reed, M. D., McDonald, J.D. (2010) Inhaled diesel emissions alter atherosclerotic plaque composition in ApoE(−/−) mice. Toxicol. Appl. Pharmacol. 242, 310–317.
5 Durga, M., Devasena, T., Rajasekar, A. (2015) Determination of LC50 and sub-chronic neurotoxicity of diesel exhaust nanoparticles. Environmental Toxicology and Pharmacology. 40 (2), 615–625. (referentie weg)
6 Valberg et al. (2008) (referentie weg)
7 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3612296/ (referentie weg)
8 Pope CA 3rd, Burnett RT, Thurston GD, Thun MJ, Calle EE, Krewski D, Godleski JJ: Cardiovascular mortality and long-term exposure to particulate air pollution: epidemiological evidence of general pathophysiological pathways of disease. Circulation 2004, 109(1):71–77.
9 Moulton P V, Yang W. (2012) Air pollution, oxidative stress, and Alzheimer’s disease. Journal of Environmental and Public Health.
10 Markesbery, W. R. (1999) The role of oxidative in Alzheimer disease. Arch Neurol. 56 (12), 1449-52.
12 H. Poon, V. Calabrese, G. Scapagnini, and D. Butterfield, “Free radicals and brain aging,” Clinics in Geriatric Medicine, vol. 20, no. 2, pp. 329–359, 2004.
13: Nussbaum: A tale of two prions
14: L. Migliore and F. Coppedè, “Environmental-induced oxidative stress in neurodegenerative disorders and aging,” Mutation Research, vol. 674, no. 1-2, pp. 73–84, 2009.
16 Lull, M. E., Block, M. L. (2010) Microglial activation and chronic neurodegeneration. Neurotherapeutics, 7(4), 354-365.
20 Haass, C., Kaether, C., Thinakaran, G. & Sisodia, S. Trafficking and proteolytic processing of APP. Cold Spring Harb. Perspect. Med. 2, a006270 (2012).
21 Dhuria, S. V., Hanson, L. R., & Frey, W. H. Intranasal delivery to the central nervous system: mechanisms and experimental considerations. Journal of pharmaceutical sciences, 2010 99(4), 1654-1673.
22 Luchinni 2012, primair artikel
23 Genc S, Zadeoglulari Z, Fuss S H, Genc K. The adverse effects of air pollution on the nervous system. Journal of Toxicology, 2011
Air pollution exposure, cause-specific deaths and hospitalizations in a highly polluted Italian region .nih.gov.proxy.library.uu.nl/pubmed/?term=Air+pollution+exposure%2C+cause-specific+deaths+and+hospitalizations+in+a+highly+polluted+Italian+region
Exposure to air pollution and cognitive functioning across the life course – A systematic literature review
...(download the rest of the essay above)