Brainstem cytokine changes in healthy ageing and Motor Neurone Disease

Journal of the Neurological Sciences 381 (2017) 192–199

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Brainstem cytokine changes in healthy ageing and Motor Neurone Disease Anuradha Tennakoon, Viythia Katharesan, Ian P. Johnson ⁎ Discipline of Anatomy and Pathology, The University of Adelaide, SA5005, Australia

a r t i c l e

i n f o

Article history: Received 24 February 2017 Received in revised form 12 July 2017 Accepted 8 August 2017 Available online 10 August 2017 Keywords: Motor Neurone Disease Cytokines MIP-1β Astrocytes Inflammation Ageing

a b s t r a c t Neuroinflammation is linked to healthy ageing, but its role in the development of age-related neurodegenerative diseases is unclear. In this pilot study we used a multiplex assay approach to compare 27 cytokines in 6 young adult and 6 ageing control brainstems with those in 6 MND brainstems. We report that healthy ageing is associated with significantly increased brainstem levels of IL-1β, IP-10 and MIP-1β which co-localise immunocytochemically to astrocytes. MND brainstem is also characterised by a general increase in both pro- and anticytokine levels, but fails to show the expected age-related increase in MIP-1β and IP-10. This pilot study is the first to show that MND is associated with a failure of specific features of the normal age-related neuroinflammatory process. We suggest that our pilot data indicates that neuroinflammation during healthy ageing may not always be detrimental to motoneuronal survival and that age-related neurodegenerative diseases, such as MND, may instead result from defective neuroinflammation. © 2017 Elsevier B.V. All rights reserved.

1. Introduction Motor Neurone Disease (MND) includes several neurodegenerative conditions, namely Amyotrophic Lateral Sclerosis, Progressive Bulbar Palsy, Primary Lateral Sclerosis and Progressive Muscular Atrophy, which are characterised by the loss of upper and lower neurons either singly or in combinations [1]. The single biggest risk factor for approximately 90% of cases (sporadic MND) is advancing age [2]. A characteristic of ageing is an imbalance of inflammatory and anti-inflammatory processes due to dysregulation of initiation and resolution of innate and adaptive immune mechanisms resulting in an overall low-grade pro-inflammatory status in a process that has been termed ‘inflammaging’ [3–5]. This process also affects the ageing central nervous system (CNS) [6,7], and this has led to the suggestion that aberrant CNS inflammation might underlie the development of age-related neurodegenerative diseases, including MND [8,9] ([10,11], McGeer and [12–15]). Although inflammation involves a complex interplay between multiple chemical mediators [16,17], most studies of inflammation in MND have focussed on increases in single pro-inflammatory cytokines and chemokines, or where multiple cytokines have been studied [18– 21], these have focussed on cerebrospinal fluid (CSF) rather than CNS tissue. In MND, elevated levels of IL-1β, IL-2, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12, IL-13, IL-15, IL-17A, G-CSF, FGF, eotaxin, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-α, MIP-1β, RANTES and TNF-α have been reported in cerebrospinal fluid (CSF), serum or spinal cord tissue (Refer Table 1 ⁎ Corresponding author. E-mail address: [email protected] (I.P. Johnson).

http://dx.doi.org/10.1016/j.jns.2017.08.013 0022-510X/© 2017 Elsevier B.V. All rights reserved.

for references). However, decreased levels of IL-1β IL-2, IL-5, IL-10, FGF, IFN-γ and VEGF have also been found in MND patients (Table 1). Other studies have reported no change in some cytokines, such as IL4, IL-6, IP-10 and PDGF-BB (Table 1). Thus, a complicated picture emerges of changes in the overall cytokine signature in MND. The extent to which this reflects real differences or differences between different investigators and tissue samples is not clear. In addition, very few studies include young adults who have died from non-neurological causes to control for the effect of ageing per se on the inflammatory status of the CNS. In this study, we have measured levels of 27 cytokines simultaneously in post-mortem brainstem taken from patients who died from non-neurological causes aged 20– 33 years and 72–86 years and compared these with brainstem taken from patients aged 60–78 years who died from sporadic MND. We previously found that facial motoneurons in ageing rats were less likely to die following injury [44] and that this was associated with increased levels of cytokines in the brainstem where the facial nuclei are located [45]. Since, facial motoneurons are affected in MND [46] and we have animal data implicating brainstem cytokines in motoneuronal survival, the present cytokine study has been undertaken on the brainstem in MND and results compared with age-matched and young controls. 2. Methods Fresh frozen brainstem was obtained from the South Australian Brain Bank from 6 patients (mean age 67 years) who died from sporadic MND, diagnosed clinically and pathologically and from 6 patients (mean age 74 years) of similar age who died from non-neurological causes (old

A. Tennakoon et al. / Journal of the Neurological Sciences 381 (2017) 192–199 Table 1 Cytokine alterations in MND patients. Cytokine Classification based on function

Found in

IL-1ra IL-1β

Pro-inflammatory Pro-inflammatory

IL-2

Pro-inflammatory

IL-4 IL-5

Anti-inflammatory Anti-inflammatory

IL-6

Pro-inflammatory, Anti-inflammatory

CSF CSF Spinal cord tissue CSF Serum CSF CSF Serum Serum, skin CSF

IL-7

Pro-inflammatory

IL-8 IL-9 IL-10

Pro-inflammatory, Chemokine Anti-inflammatory Anti-inflammatory

IL-12

Pro-inflammatory

IL-13 1L-15 IL-17A

Anti-inflammatory Pro-inflammatory Pro-inflammatory

FGF

Growth factor

Eotaxin G-CSF

IFN-γ

Chemokine Anti-inflammatory, growth factor Pro-inflammatory, growth factor Pro-inflammatory

IP-10

Chemokine

MCP-1

Chemokine

MIP-1α MIP-1β PDGF-BB RANTES TNF-α

Chemokine Chemokine Growth factor Chemokine Pro-inflammatory

GM-CSF

VEGF

Growth factor

Table 2 Details of the fresh frozen brain samples used for cytokine assays. Level of cytokine in MND

Method

↓ ↓ ↑

MP1 MP1 RP2, MP3

↑ ↓ – ↑ ↓ ↑ ↑ – ↑ – ↑ – ↑ ↓ – ↑ – ↑ ↑ ↑ ↑ ↓

EL10, MP5 MP6 MP1 MP12 MP6 MP6, EL7, 8, 10 EI4, EL8, MP5 IS9, MP1 MP3 MP1 MP3, 6, 11 MP1 MP3 MP12 MP1 MP10 EL13, MP3 EL14, FC15 EL14, MP5, 10 MP1, 3, 5, 10, EL14, 16, 17 RP18

↑ ↑ ↑

MP3, 5, 10 MP3,11 MP3, 5, 10, 12

CSF, serum

↑

MP5, 10

Blood, CSF, serum Plasma CSF CSF Spinal cord tissue, CSF, serum CSF, serum CSF CSF CSF, serum Serum plasma, blood CSF CSF CSF, serum

↑

WB19, EL21, 24, MP3,10 EL20 MP11 MP3 RP22, EL10, 23, 25 , MP3, 10, 11

Spinal cord tissue

– ↓

CSF CSF CSF CSF CSF CSF CSF Serum CSF Serum, CSF Serum Serum, CSF Serum, spinal cord tissue, CSF Spinal cord tissue CSF, serum CSF, serum CSF, serum

193

↓ – ↑ ↑

↑ ↑ – ↑ ↑

MP10, EL26 MP3, 5, 10 MP3,10 EL27, MP3 EL8, 24, 28, 29

– ↑ ↑

MP1 MP3 EL30, RT31, MP3, 5, 10 EL30, MP1 EL32

(1) [19], (2) [22], (3) [21], (4) [23], (5) [20], (6) [24], (7) [25], (8) [73], (9) [26], (10) [18], (11) [27], (12) [28], (13) [29], (14) [30], (15) [31], (16) [32], (17) [33], (18) [34], (19) [35], (20) [36], (21) [37], (22) [10], (23) [38], (24) [74], (25) [13], (26) [39], (27) [40], (28) [41], (29) [42], (30) [75], (31) [76], (32) [43]. ↑ = significantly increased compared to controls, ↓ = significantly decreased compared to controls, − = no change or not detected, MP = multiplex assay, EL = enzyme-linked immunosorbent assay, RP = real time qPCR, EI = electro-immunoassay, WB-western blot, FC = flow cytometry, IS-immunostaining.

control). Fresh frozen brainstem from 6 young adults (mean age 27.2 years) who died from non-neurological causes (young control) was obtained from the Edinburgh Brain Bank, UK (Table 2). The histology of the pons of the MND and age-matched controls were considered normal for the age of the subject. No neuropathological data was available for the young controls.

n

Mean age (range) years Male:female Mean post mortem interval (range) hours Cause of death [duration of MND (years), disease subtype]

Brain bank unique identifiers

Adult control

Ageing control

MND

6

6

6

27.2 (20−33) 1:1 36.7 (20–53)

74.0 (48–86) 2:1 30.1 (6–48)

67.0 (62–78) 1:1 24.2 (6–44)

Ischaemic heart disease Suspension by a ligature Suspension by a ligature Cardiomyopathy Suspension by a ligature Suspension by a ligature SD053/14;BBN24342 SD042/12;BBN377 SD008/12;BBN3771 SD006/10;BBN2503 SD036/08;BBN2455 SD023/08;BBN2442

Generalised arteriosclerosis MRSA Pulmonary infarct Hepatocellular carcinoma Metastatic cancer IHD

MND (1.1,ALS) MND (3, PMA) MND(14,PMA) MND(NA,ALS) MND (2,ALS) MND (2.5,ALS)

SA006 SA0112 SA0162 SA0098 SA0214 SA0230

SA0186 SA0202 SA0203 SA0212 SA0221 SA0245

PMA-Progressive Muscular Atrophy, ALS-Amyotrophic Lateral Sclerosis, NA-Data not available.

2.1. Cytokine assays Approximately 2 mm of the mid/lower pons was removed with a sterile scalpel blade, weighed then homogenised at a concentration of 0.1 g/ml in homogenisation buffer made up with PBS, triton-X and protease inhibitors (Roche, complete tablets) in grinding chambers using 10 pestle-strokes for every sample. Homogenised samples were centrifuged (1000g) for 15 min at 4 °C, the pellet discarded and the supernatant stored at − 80 °C. Bovine serum albumin standards ranging between 0.1 and 10 μg/μl in distilled water were prepared and loaded as triplicates along with blanks and samples into 96-well ELISA plates. The BioRad DC Protein Assay (BioRad, New South Wales), which is a modified Lowry method, was used to quantitate the amount of protein in each sample as per the manufacturer's instructions. Bio-Plex Pro human 27 plex cytokine assay kits (BioRad, New South Wales) were used to measure the concentration of 27 cytokines within each sample. The panel was composed of Interleukin-1β (IL-1β), IL-1 receptor antagonist (IL-1Ra), IL-2, IL-4, IL-5, IL-6, IL-7, IL-8/CXCL8, IL-9, IL-10, IL-12 (p70), IL-13, IL-15, IL-17, eotaxin/CCL11, fibroblast growth factor-2 (FGF-2), granulocyte colony-stimulating factor (G-CSF), granulocyte macrophage colony-stimulating factor (GM-CSF), interferon-γ (IFN-γ), IFN-γ-induced protein 10 (IP-10)/CXCL10, monocyte chemotactic protein-1 (MCP-1)/CCL2, macrophage inflammatory protein-1α (MIP1α)/CCL3, macrophage inflammation protein-1β (MIP-1β)/CCL4, platelet-derived growth factor BB (PDGF-BB), regulated on activation, normal T cell expressed and secreted (RANTES)/CCL5, tumour necrosis factor-α (TNF-α), and vascular endothelial growth factor (VEGF). Depending on the ability of these cytokines to increase or decrease the inflammatory response they are categorised as being pro-inflammatory or anti-inflammatory respectively [47]. Of the cytokines studied here, IL1ra, IL-1β, IL-2, IL-7, IL-8, IL-12, IL-15, IL-17A, IFN-ϒ, TNF-α, MIP-1α, MIP-1β, MCP-1, IP-10, RANTES, GM-CSF, VEGF and eotaxin are pro-inflammatory, whereas IL-4, IL-5, IL-9, IL-10, IL-13, FGF, G-CSF and PDGF-BB are anti-inflammatory. IL-6 has shown both pro-inflammatory and anti-inflammatory effects [48]. Samples were loaded onto 96 well plates in duplicates (3 m and 12–18 m rats) and triplicates (24 m rats). Plates were read using a Magpix Luminex multiplexing platform (Abacus-ALS, Queensland, Australia). Experimental data was calibrated against standard samples of all 27 cytokines (BioRad, New South Wales, Australia).

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Table 3 Antibodies and dilutions used in immunocytochemistry.

1 Antibodies

2 Antibodies

Antibody

Dilution Source

Polyclonal goat anti human IL-1β Polyclonal goat anti human MIP-1β Polyclonal goat anti human IP-10 Polyclonal rabbit anti human GFAP Monoclonal donkey anti goat (Alexa Fluor® 647) Monoclonal donkey anti rabbit (Alexa Fluor® 488) Monoclonal donkey anti mouse (Alexa Fluor® 488)

1:100 1:10 1:100 1:5000 1:200

R&D Systems, USA Abcam, UK

1:200 1:200

2.2. Immunostaining After removing tissue for cytokine analysis, the remaining brainstem was cryosectioned at 10 μm onto slides and air-dried. Sections were then fixed in 4% paraformaldehyde in 0.1 M sodium phosphate-buffered saline (PBS) for 15 min, then rinsed twice in 0.1 M PBS for 10 min, followed by section permeabilisation with 1% Triton X-100 in PBS for 15 min. Slides were then incubated in 2% Bovine Serum Albumin in PBS for 20 min to prevent non-specific binding of antibodies. After rinsing with 1% Triton X-100 in PBS for 10 min, slides were incubated with primary antibodies to IL-1β, MIP-1β, IP-10 and GFAP (Table 3) overnight at 4 °C. The following day, respective secondary antibodies with different fluorescent tags (Table 3) were applied for 30 min in the dark. To block autofluorescence, slides were immersed in 0.3% Sudan Black in 70% ethanol [49] for 25 min and rinsed 3 times in 0.1 M PBS for 5 min. Finally, tissue sections were mounted on slides with Crystal Mount aqueous mountant (Sigma-Aldrich, New South Wales, Australia), coverslipped and the edges of the coverslips sealed with nail varnish. Controls included omission of either the primary or secondary antibody from the above protocol. Single random images of MIP-1β and GFAP immunostained samples were acquired at × 20 objective magnification, grey-scaled using Adobe Photoshop Lightroom and mean grey levels determined using ImageJ. The fluorescence intensity (FI) in pixels/μm2 of each image [50] was calculated as: FI ¼

integrated density−ðarea of image x average mean gray value of backgroundÞ area of image

2.3. Statistical analysis Statistical analysis was undertaken using the SPSS statistics 22 programme (IBM). The Kruskal Wallis test was used for preliminary data analysis. When the differences were significant (p b 0.05), Dunn's multiple comparison post-hoc tests were conducted for pairwise comparisons to evaluate the significance of differences between the three

groups. To counter the otherwise inflation of type 1 error with two pairwise comparisons, the Bonferroni-corrected p-value of 0.025 (initial p-value of 0.05 divided by 2 comparisons) was taken to indicate statistical significance. Data in charts is represented as mean + SEM. The study was approved by the University of Adelaide Human Ethics Committee (H-2015-044). 3. Results and discussion Significant changes were found in concentrations of IL-1β (p = 0.003), IP-10 (p = 0.02) and MIP-1β (p = 0.004, Kruskal Wallis). Post-hoc testing showed that IL-1β, IP-10 and MIP-1β concentrations were significantly increased in the brainstem of ageing compared to adults (IL-1β: 7.4 ± 1.3 pg/ml vs. 2.5 ± 0.4 pg/ml, p = 0.017; IP-10: 980.7 ± 563.6 pg/ml vs. 78.5 ± 10.2 pg/ml, p = 0.020; MIP-1β: 54.2 ± 16.6 pg/ml vs. 18.0 ± 1.3 pg/ml, p = 0.006) (Fig. 1). When compared to adults, levels of IL-1β, IL-1ra and IL-6 were also significantly elevated in MND brainstems (IL-1β: 2.5 ± 0.4 vs. 8.2 ± 1.2 pg/ml, p = 0.0062, IL1ra: 172.1 ± 16.4 pg/ml, p = 0.0148, IL-6: 23.9 ± 6.1 vs 86.9 ± 11.7 pg/ml, p = 0.0062), but there was no significant increase in IP10. IL-1β and IP-10 levels did not differ significantly between ageing and MND brainstems. While there is clearly a large difference between the mean values of IP-10 between the ageing control and MND samples, our failure to find a significant difference is likely due to the large variability of the ageing IP-10 sample and the fact that the Bonferroni correction for multiple samples reduced statistical significance to from the usual p b 0.05 to p b 0.025. While IP-10 levels in MND brainstems were 5-fold lower than ageing controls, this was associated with considerable variability and with the present small sample size was not statistically significant. MIP-1β concentrations were significantly decreased in MND patients compared to ageing (29.3 ± 2.1 pg/ml vs. 54.2 ± 16.610 pg/ml, p = 0.023). For the remaining 24 cytokines, the Kruskal Wallis test indicated significant changes in cytokine concentrations of IL-6 (p = 0.008), IL-1ra (p = 0.012), TNF-α (0.035), IL-8 (0.036), IL-5 (p = 0.039), IL-2 (p = 0.04) and IL-4 (p = 0.045) between all 3 groups, but Dunn's multiple comparison post-hoc test failed to show any significance between individual groups (Figs. 2 and 3). MIP-1β immunostaining co-localised with GFAP-positive astrocytes in adult, ageing and MND brainstem tissue (Fig. 4). Qualitative analysis indicated that all GFAP positive cells co-localised with MIP-1β. In contrast, IL-6, IP-10 or IL-1β did not show any co-localisation with astrocytes (data not shown). The fluorescence intensity of MIP-1β staining was significantly increased in ageing brainstem compared to adult controls (15.0 ± 1.3 pixels/μm2 vs. 4.5 ± 2.3 pixels/μm2; p = 0.0038) and significantly decreased (15.0 ± 1.3 pixels/μm2 vs. 6.1 ± 2 pixels/μm2; p = 0.0197) in MND brainstems compared to ageing controls (Fig. 5). This is in line with the multiplex results for cytokines. There was no significant

Fig. 1. Changes in IL-1β, IP-10 and MIP-1β cytokine concentrations in adult, ageing and MND brainstem. IL-1β, IP-10 and MIP-1β levels are significantly higher in ageing controls compared to adult controls, whereas MIP-1β levels are significantly lower in MND compared to ageing controls.

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Fig. 2. Remaining 24 brainstem cytokine concentrations in adult, ageing and MND brainstem. Significant differences are seen between all 3 groups for 7 cytokine -1ra, IL-6, TNF-α, IL-8, IL15, IL-2 and IL-4 but post-hoc tests failed to show significant differences for individual cytokines between groups.

change in the fluorescence intensity of GFAP (astrocyte) staining between adult and ageing (11.1 ± 1.8 pixels/μm2 vs. 14.6 ± 1.8 pixels/ μm2; p N 0.025) or ageing and MND brainstem tissue (14.6 ± 1.8 pixels/μm2 vs. 14.9 ± 3.3 pixels/μm2; p N 0.025) (Fig. 6).

4. Discussion This pilot study shows that brainstem levels of IL-1β, IP-10 and MIP1β are increased with normal ageing, but that MND is associated with a

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Fig. 3. Dot-plot graphs of 27 brainstem cytokine concentrations in adult, ageing and MND brainstem.

Fig. 4. MIP-1β expression in GFAP-positive astrocytes in adult, ageing and MND mid/lower pons. Note that it is not possible to assess changes in overall GFAP or MIP-1β immunostaining intensity in the small areas shown here (see Figs 5 and 6).

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Table 4 Sources and functions of cytokines. Cytokine Function

Source

IL-1β

Monocytes, macrophage, astrocytes, microglia ([51], [52], [53])

IP-10

MIP-1β

Fig. 5. Intensity of MIP-1β immunostaining. MIP-1β fluorescence intensity is increased in ageing control brainstem compared to adult controls, but not in MND brainstem.

failure of MIP-1β to increase. This runs counter to the popular view that age-related increases CNS inflammation (neuroinflammation) are detrimental to neuronal survival and may underlie the development of agerelated neurodegenerative diseases. Instead, it points towards neuroinflammation being an adaptive and protective response by the CNS to ageing and that defects in the neuroinflammatory process rather than neuroinflammation per se might underlie the development of age-related neurodegenerative diseases, such as MND. It must be acknowledged, however, that this conclusion will clearly need confirming with a larger sample than used in this pilot study. The Table 4 summarises the functions and sources of IL-1β, IP-10 and MIP-1β cytokines. Age-related increases in IL-1β have been reported in the rat cerebral cortex and dentate gyrus [57,58] which is consistent with the results of the present study. However, both high levels [21] and low levels [19] of IL-1β have been reported in the CSF of MND patients. These discrepancies in results could be attributed to varying tissue types studied (CSF vs. brainstem) and age ranges of subjects compared to MND individuals (21–84 years, 32–75 years vs. 63–86 years). Increased levels of IP-10 have been found in the cortex and hippocampus of 17-month mice [59], consistent with the age-related increase in this cytokine in the present study. In line with a previous report [27], we also found no

Fig. 6. Intensity of GFAP immunostaining. No significant changes in GFAP fluorescence intensity are found between the groups.

Acts on glia to induce production of other cytokines and growth factors regulating inflammation in brain [51] Chemoattractant for activated T cells [54]

Leukocyte recruitment to sites of inflammation [55]

Endothelial cells, fibroblasts, keratinocytes, mesangial cells, monocytes, neutrophils and astrocytes [54] Monocytes, T lymphocytes, natural killer cells, dendritic cells, neutrophils, microglia, brain micro vessel endothelial cells and vascular smooth muscle cells [56]

differences in IP-10 levels between controls and MND, although it is possible that larger sample sizes may have revealed a significant decrease in IP-10 compared to age-matched controls. This is the first report of decreased MIP-1β levels in MND brainstem and it contrasts with reports of increased levels of MIP-1β in CSF in MND [18,20,21]. These inconsistencies in results may be due to the varying sample sizes, tissue types and age ranges of the subjects. Although in one of these studies [21], a negative correlation was found between CSF levels of MIP-1β and disease progression, suggesting that in later stages of the disease, lower levels of MIP-1β may be associated with motoneuronal death. In contrast to our findings, in a TDP-43 transgenic mouse model of ALS, upregulation of the MIP-1β gene was found in spinal motoneurones suggesting that MIP-1β is associated with motoneuronal death [55]. The extent to which our data on brainstem MIP-1β can be compared with data on CSF or animals models is therefore MIP-1β unclear. In the periphery, there is evidence that T cells produce MIP-1β and that this is elevated with advancing age in mice [60], whereas we show here that astrocytes are the likely source of MIP-1β in the CNS. MIP-1β has also been primarily localised to astrocytes in control and Alzheimer's disease brain [61]. Hence, there is a possibility that astrocytes may be the primary cause in MND however, further studies are needed to confirm this. Notwithstanding the cellular source of cytokines, there is evidence from experimental animals for the direct passage of inflammatory mediators or cells into the CNS [62,63], and movement from the CNS to the periphery must also be presumed. MIP-1β is recognised by CCR5 receptors which are present in both neurons and glia, leading to the suggestion that it may mediate neuroneglial interactions [64,65]. Disturbances to this interaction can lead to neuronal death either through altered trophic support or disturbance of the tripartite synapse [66,67]. It is possible, therefore, that elevated MIP-1β levels may serve to prevent age-related neuronal damage and a failure of the normal age-related elevation of MIP-1β in MND may render motoneurones vulnerable to damage and death. Ageing has been linked to an imbalance of inflammatory and anti-inflammatory processes due to dysregulation of initiation and resolution of innate and adaptive immune mechanisms resulting in an overall low-grade pro-inflammatory status in a process that has been termed ‘inflammaging’ [3–5]. While most studies have focussed on changes in serum levels of pro-inflammatory cytokines such as interleukins 5, 6 and 8 (IL-5, IL-6, IL-8) and tumour necrosis factor alpha (TNFα), there is also evidence of age-related changes in the inflammatory status of the brain [6,7]. Some forms of CNS inflammation may be secondary to peripheral inflammatory events and can involve the direct passage of inflammatory mediators or cells into the CNS [62,63]. Other forms of CNS inflammation may be due to direct activation of cells intrinsic to the CNS such as microglia and astroglia [68,69]. More recently, microglial activation has been linked to the CNS inflammation associated with age-related neuronal degeneration such as Alzheimer's Disease (AD) [8,9], Parkinson's Disease (PD) [11] and Motor Neurone Disease

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(MND) [10,12,14,15,70]. These studies, however, have generally focussed on just one or two cytokines, which does not allow consideration of changes in the overall balance of pro- and anti-inflammatory cytokines in the possible pathogenesis of these diseases. Neither does it allow discussion of the possible interactions between cytokines that affect their actions [17]. There is emerging evidence from animal studies that the inflammatory response around injured and ageing motoneurones may be protective in some circumstances [71] [45]. This appears contrary to the prevailing view that inflammatory responses are generally associated with neurodegeneration [6,72]. These two views may be reconciled by considering age-related neurodegeneration as being linked to an aberant response of the immune system to the process of ageing, rather than a global increase in inflammatory levels. This alternative view of ‘inflammaging’ in the context of the pathogenesis of neurodegenerative disease would include increases in some, but not all inflammatory mediators but also include a failure of other inflammatory mediators to show a normal age-related increase. The low sample size of 6 was a limitation of the project, dictated primarily by the difficulty of obtaining young adult control tissue. Nevertheless, the statistical power for the present study was calculated to be 77.3%, indicating that the results reported are likely to be reproducible with a large sample size. The present study, however, involved multiple comparisons and as such there is a possibility of obtaining a significant difference by chance. To correct for this, the current significance value can be modified using the Bonferroni-Dunn correction. This requires the value for statistical probability to be reduced from the current reported value of b0.025 to p b 0.016, indicating that our present pilot study results must strictly be taken as indicative of trends. A largerscale study is underway. In addition, since we sampled the mid/lower pons, it is likely that different samples would be at different levels of the pons, raising the possibility that any rostro-caudal differences in the disease process may have affected the results. 5. Conclusions This pilot study is the first to show that MND is associated with a failure of specific features of the normal age-related neuroinflammatory process. Notwithstanding our small sample size, we suggest this pilot data indicates that neuroinflammation during healthy ageing may not always be detrimental to motoneuronal survival and that age-related neurodegenerative diseases, such as MND, may instead result from defective neuroinflammation. Acknowledgements We acknowledge financial support from The University of Adelaide HDR scholarship (VK) and honours (AT) programmes and consultancy funds (IJ). We are most grateful to the South Australian Brain Bank and the Edinburgh Brain Bank for providing post-mortem material. References [1] W.R. Brain, J.N. Walton, Brain's diseases of the nervous system, Oxford U.P, London, New York, 1969. [2] G. Logroscino, B.J. Traynor, O. Hardiman, A. Chio, D. Mitchell, R.J. Swingler, A. Millul, E. Benn, E. Beghi, Incidence of amyotrophic lateral sclerosis in Europe, J. Neurol. Neurosurg. Psychiatry 81 (2010) 385–390. [3] M. Deleidi, M. Jaggle, G. Rubino, Immune aging, dysmetabolism, and inflammation in neurological diseases, Front. Neurosci. 9 (2015) 172. [4] C. Franceschi, J. Campisi, Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases, J. Gerontol. A Biol. Sci. Med. Sci. 69 (Suppl. 1) (2014) S4–9. [5] E. Montecino-Rodriguez, B. Berent-Maoz, K. Dorshkind, Causes, consequences, and reversal of immune system aging, J. Clin. Invest. 123 (2013) 958–965. [6] S. Amor, L.A. Peferoen, D.Y. Vogel, M. Breur, P. Van Der Valk, D. Baker, J.M. Van Noort, Inflammation in neurodegenerative diseases—an update, Immunology 142 (2014) 151–166. [7] J.G. Sheng, R.E. Mrak, W.S. Griffin, Enlarged and phagocytic, but not primed, interleukin-1 alpha-immunoreactive microglia increase with age in normal human brain, Acta Neuropathol. 95 (1998) 229–234.

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