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Cognitive deficit is related to immune-cell beta-NGF in multiple sclerosis patients
Journal of the Neurological Sciences 321 (2012) 43–48
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Cognitive deficit is related to immune-cell beta-NGF in multiple sclerosis patients Alicja Kalinowska-Łyszczarz a, b,⁎, Mikołaj A. Pawlak c, Sławomir Michalak a, d, Jacek Losy d, e a
Department of Neurochemistry and Neuropathology, Chair of Neurology, Poznan University of Medical Sciences, Poland Heliodor Swiecicki University Hospital, Poznan, Poland c Department of Neurology and Cerebrovascular Disorders, Poznan University of Medical Sciences, Poland d Neuroimmunological Unit, Institute of Experimental and Clinical Medicine, Polish Academy of Sciences, Poland e Department of Clinical Neuroimmunology, Chair of Neurology, Poznan University of Medical Sciences, Poland b
a r t i c l e
i n f o
Article history: Received 1 March 2012 Received in revised form 29 May 2012 Accepted 19 July 2012 Available online 9 August 2012 Keywords: Multiple sclerosis Cognitive dysfunction Neuroimmunology Neurotrophic factors Neurotrophins Neuropsychology
a b s t r a c t Objectives: Multiple sclerosis (MS) is a chronic inflammatory demyelinating and neurodegenerative disease of the central nervous system (CNS), causing cognitive impairment in 45–65% of patients. Beta-NGF facilitates proper cholinergic transmission in the healthy CNS. In MS-damaged tissue there is a relative deficit of neurotrophins that might be compensated by peripheral blood mononuclear cells (PBMCs) synthesis. Our aim was to evaluate the relationship between PBMCs neurotrophins' expression and cognitive performance in relapsing–remitting MS (RRMS) patients. Patients and methods: Beta-NGF, NT-3 and NT-4/5 levels were measured in sera and in PBMCs by ELISA method in 41 RRMS patients in remission. All patients underwent neuropsychological assessment with a battery of 10 tests evaluating a wide range of cognitive functions. Results: PBMCs beta-NGF concentration correlated significantly with spontaneous word list generation test (Pearson R = 0.37, p = 0.02) and 15-Word List Recall Test results (Pearson R = 0.40, p b 0.00). Both tests assessing semantic memory correlated significantly with the cognitive composite score, defined as a number of tests in which patients performed below group median for the given test. Conclusions: In RRMS beta-NGF is strongly linked to cognitive performance, which makes it an attractive therapeutic target. It might play a neuroprotective role in MS, especially in the cognitive domain. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Multiple sclerosis (MS) has originally been considered a chronic inflammatory demyelinating disease of the central nervous system (CNS), affecting primarily the white matter of the brain and spinal cord, and leading to inevitable accumulation of disability in patients. The original definition has changed over years and it is now established that while driven by autoimmune inflammatory reaction, the disease also develops a neurodegenerative component [1]. Autoimmune reaction may in fact play a protective role in the CNS of MS patients. CD4+ Th2 cells produce anti-inflammatory cytokines, and peripheral blood mononuclear cells (PBMCs) secrete neurotrophic factors, including neurotrophins, namely brain-derived neurotrophic factor (BDNF), beta nerve growth factor (beta-NGF), neurotrophin 3 (NT-3) and neurotrophin 4/5 (NT-4/5). In MS additional support from PBMCs might compensate the relative neurotrophic deficiency in the damaged CNS tissue. Failure to produce the adequate neurotrophic factors' concentrations might result in impaired regeneration within the CNS, ⁎ Corresponding author at: Department of Neurochemistry and Neuropathology, Chair of Neurology, Poznan University of Medical Sciences, ul. Przybyszewskiego 49, 60‐355 Poznan, Poland. Tel.: +48 61 869 1454; fax: +48 61 869 1697. E-mail address: [email protected] (A. Kalinowska-Łyszczarz). 0022-510X/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jns.2012.07.044
consequently leading to more pronounced atrophy, which is the main determinant of MS patients' end-point disability. Cognitive dysfunction occurs in approximately 45 to 65% of MS patients [2,3], independently of disease course, duration, clinical disability or total demyelinating lesion volume measured in magnetic resonance imaging (MRI) studies. Cognitive functions that are typically impaired in MS patients include: attention, visuo-spatial analysis, abstract reasoning, multi-tasking, operative and semantic memory, and information processing speed [3,4]. Such pattern is consistent with disconnection syndrome that is observed in disruption of white matter pathways similar to the pattern occurring in subcortical dementia [5]. In the adult brain neurotrophins facilitate synaptic plasticity, which is crucial for memory and other cognitive domains. BDNF, NGF, NT-3 and their receptors, tropomyosin related kinase B (TrkB) and TrkC, are expressed at relatively high levels in the adult hippocampus [6]. NGF has been shown to exert a protective effect on the cholinergic system in animal models [6–8]. BDNF deficiency may lead to reduction in hippocampal synaptic plasticity [9], and exogenous NT-3 supply improves cognitive skills in rats [10]. MS-associated cognitive dysfunction may thus be related to inadequate neurotrophin production within the CNS. This seems especially plausible in light of animal models research findings, i.e. the fact that cognitive deficit has been associated with NGF-dependent cholinergic deficiency in cerebral cortex, hippocampus
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and basal forebrain of rats with experimental allergic encephalomyelitis, which is an animal model for human MS [11]. So far there have only been a few studies showing the potential link between neurotrophins and cognitive functions in MS patients, all with regard to BDNF only [12,13]. Since neurotrophins have been implicated in memory acquisition and retention, we hypothesize that they exert neuroprotective role in MS, especially in the cognitive domain. We have previously shown that higher immune cell NT-3 expression is associated with reduced brain atrophy in relapsing–remitting MS (RRMS) patients, as measured by brain-parenchymal fraction (BPF) and corpus callosum crosssectional area in MRI studies [14]. In this paper, on the other hand, we present data suggesting that PBMCs expression of another neurotrophin, namely beta-NGF, is associated with better performance in neuropsychological tests in RRMS patients. 2. Patients and methods 2.1. Patient population Fourty-one patients, including 25 females and 16 males, with clinically definite RRMS, according to revised (2005) McDonald criteria [15], were recruited for the study and written informed consent was obtained from all the participants. The study protocol was approved by the Internal Review Board at Poznan University of Medical Sciences. Exclusion criteria included: current relapse stage or relapse within the last 8 weeks, steroid treatment within the last 3 months, immunomodulatory therapy in the last year, age older than 65 years, concomitant psychiatric disease, current use of antidepressants, neuroleptics or antiepileptic drugs, alcohol or drug abuse, diagnosis of another autoimmune or neoplastic pathology, relevant motor deficit in the dominant hand, upper limb ataxia or loss of visual acuity, dementia or mental impairment. All patients underwent 1.5 T MRI with the following sequences: Magnetisation Prepared Rapid Acquisition Gradient Echo (MPRAGE), FLAIR space and diffusion tensor imaging, as described in our previous manuscript [14]. Brain-parenchymal fraction (BPF) and corpus callosum cross-sectional area (CCA) were calculated according to the methodology published previously [14]. Clinical characteristics of the examined group, including data from image analysis, are presented in Table 1. 2.2. Laboratory protocol Blood was taken from patients to obtain sera and PBMCs samples. PBMCs were isolated using Ficoll–Paque (GE Healthcare) gradient method. Samples were kept frozen at −80 °C until further analysis. Before measurement of neurotrophin concentrations, PBMCs were resuspended in a lysis buffer (NaCl, Tris–HCl, EDTA), supplemented
with Triton X-100 (Sigma-Aldrich) and a proteinase inhibitor cocktail (Sigma-Aldrich). The protein content was determined using the Lowry method [16]. Beta-NGF, NT-3 and NT-4/5 levels were measured in sera and PBMCs by ELISA method, according to manufacturer's instructions (RayBio Human Beta-NGF ELISA Kit, RayBiotech, RayBio Human NT-4 ELISA Kit, RayBiotech, ChemiKine NT-3 Sandwich ELISA kit, Millipore). In PBMCs their concentrations were expressed as relevant weight units per one milligram of the protein (pg/mg protein for NT-3 and fg/mg protein for beta-NGF). The detection limits for beta-NGF, NT-3 and NT-4/5 were less than 14.0, 7.8 and 2.0 pg/ml, respectively.
2.3. Neuropsychological tests battery After full neurological examination, including EDSS (Expanded Disability Status Scale) scoring, all patients were subject to neuropsychological examination, which included 10 following tests: A. spontaneous word list generation test, which is a modified version of phonemic naming task from Brief Repeatable Battery of Neuropsychological Tests [17], B. 15-Word List Recall Test (15-WLRT), which is a modification of Rey Auditory Verbal Learning Test and California Verbal Learning Test, and comprises memorizing a list of 15 unrelated words, as in the Rey test, but in the visual modality, in a single trial and with no distractors [18], C. simple reaction time (RT) test, created with the use of PEBL software [19], D. pattern recognition test [20], E. attention to detail test [20], F. Raven's Coloured Progressive Matrices [18], G. Trail Making Tests (TMT), versions A and B [21], and H. visual digit span forward and auditory digit span forward. A short characterization of the cognitive functions evaluated by the tests is depicted in Table 3, alongside descriptive statistics of patients' results. Patients were examined by the same clinician, under similar conditions (room, daytime, and lighting) and using identical test order. Factors that could potentially influence patient's performance (i.e. headache after lumbar puncture, tiredness, and other) were excluded before examination. Time interval between blood sampling, neuropsychological and neurological assessment was within 5 days. Handedness was assessed according to Edinburgh Handedness Inventory [22], severe depression was ruled out with the use of Beck Depression Inventory [23], and the quality of life was assessed with the Polish adaptation of Ferrans and Powers Quality of Life Index [24]. Level of education was described as a number of education years. We defined results obtained in each neuropsychological (NP) test as deficient, when they were worse than the median for the given test. Therefore, the cognitive composite score had a range from 0 (all NP test results above median) to 10 (all NP test results below median).
Table 1 Descriptive statistics for clinical data of the examined RRMS group.
Age (years) Disease duration (years) EDSS BPF Total white matter lesion volume (cm3) Corpus callosum cross-sectional area (CCA, mm²) Education (years)
Mean ± SD or *Median (IQR) n = 41
Range
39 ± 11 *3 (0.65–10) *1.5 (0.25–2.38) 0.77 ± 0.03 *3.36 (0.92–13.56)
18–63 0.08–24 0–8.5 0.706–0.809 0.15–57.14
606.63 ± 134.16
318.75–862.5
*12 [10–17]
8–17
For normally distributed variables mean, standard deviation and range are provided. For ordinal variables and non-normally distributed continuous variables median, interquartile range (IQR) and range are provided — these are preceded by * mark.
2.4. Statistical analysis Analysis was performed in R CRAN statistical environment (www. r-project.org; R Development Core Team, R Foundation for Statistical Computing, 2010). Distribution of variables was evaluated using Lilliefors test [25]. Summary statistics for continuous normally distributed variables was calculated as mean, standard deviation and range. For ordinal variables and non-normally distributed continuous variables median, first and third quartiles and range were calculated. Spearman rank correlation coefficients were calculated to assess the association between neuropsychological performance results and neurotrophin concentrations. Multiple regression models were developed using clinical data, neurotrophin concentrations and neuropsychological data.
A. Kalinowska-Łyszczarz et al. / Journal of the Neurological Sciences 321 (2012) 43–48 Table 2 NT-3 and beta-NGF detection in PBMCs samples from RRMS patients. Neurotrophin
mean ± SD
Range
Detection n (%)
PBMC NT-3 (pg/mg protein) PBMC beta-NGF (pg/mg protein)
590.1 ± 327.52 1.26 ± 0.8
55.72–1710.39 0.33–3.74
38 (93%) 41 (100%)
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Table 4 Correlation matrices for PBMC NT-3 and beta-NGF concentrations and neuropsychological tests results. Pearson or Spearman* correlation coefficient r (p) NT-3 PBMC (pg/mg protein)
Since variables are distributed normally, mean, standard deviation (SD) and range are provided.
3. Results 3.1. Neurotrophin expression in sera and in PBMCs Beta-NGF, NT-3 and NT-4/5 were detected in 25 (61%), 15 (36.6%) and 32 (78.1%) of the 41 sera samples, respectively. In PBMCs beta-NGF was detected in all the samples, with the median concentration 1.07 pg/mg protein; NT-3 was found in 93% of samples, with the median concentration of 557.8 pg/mg protein; and NT-4/5 PBMCs detection was only 24.4%. Further statistical analysis was performed only when neutrophin detection was at the level of at least 80% for the given fraction. The summary of relevant data, namely beta-NGF and NT-3 expression in PBMCs, is presented in Table 2. Outliers were corrected with Grubbs test. As we have shown previously, NT-3 and beta-NGF levels did not correlate with patient age, gender and EDSS score, and beta-NGF concentration decreased with disease duration [14]. NT-3 and beta-NGF were positively correlated (Spearman correlation coeficcient r= 0.38, p= 0.02).
Spontaneous word list generation tests 15-Word List Recall Test TMT-A [ms] TMT-B [ms] Visual digit span Auditory digit span Attention to detail Raven's Coloured Progressive Matrices RT Q1 RT median RT IQR RT Q3
r = 0.09 (p = 0.60) r = − 0.16 (p = 0.34) r = − 0.17 (p = 0.30) *r = − 0.03 (p = 0.85) *r = 0.07 (p = 0.65) *r = 0.07 (p = 0.69) *r = 0.12 (p = 0.48) r = 0.21 (p = 0.20) r = 0.73 r = 0.04 r = 0.24 r = 0.00
(p = 0.67) (p = 0.81) (p = 0.16) (p = 0.98)
beta-NGF (pg/mg protein) r = 0.37 (p = 0.02) r = 0.40 (p = 0.00) r = − 0.12 (p = 0.45) *r = − 0.16 (p = 0.31) *r = 0.05 (p = 0.76) *r = −0.06 (p = 0.72) *r = 0.24 (p = 0.14) r = 0.19 (p = 0.23) r = 0.02 (p = 0.89) r = 0.05 (p = 0.75) r = 0.08 (p = 0.64) r = − 0.01 (p = 0.94)
For normally or non-normally distributed variables Pearson or Spearman rank correlation coefficient were calculated, respectively. Statistically significant correlation is marked with bold type. TMT — trail making test, RT — reaction time test, Q1 — first quartile, Q3 — third quartile, IQR — interquartile range.
(Spearman r = −0.77, p b 0.00), and Raven's Coloured Progressive Matrices Test (Pearson r = −0.68, p b 0.00). However, in stepwise regression model for the cognitive composite score as a dependent variable only the relationships with the following tests reached statistical significance as covariates: visual digit span, spontaneous word list generation tests, 15-WRLT, and attention to detail. This model is described by R² = 0.84 (p b 0.00). 3.3. Correlation between cognitive dysfunction and PBMC neurotrophin expression
3.2. Cognitive performance Descriptive statistics for neuropsychological test performance of the investigated RRMS group is presented in Table 3. The median results for different RT distribution parameters were as follows: RT min = 85 ms, RT Q1 (first quartile) = 309.5 ms, RT median=339.5 ms, RT Q3 (third quartile)=396.25 ms, IQR=264 ms. In the investigated group the correlation coefficient between Q1 and Q3 for RT test was r=0.87 (pb 0.00). The cognitive composite score achieved by the patients, expressed as the number of cognitive tests in which their result was below median for the given test, correlated significantly with RT distribution parameters, namely median (Pearson r = 0.33, p = 0.05) and Q3 (Pearson r= 0.35, p = 0.03), and with the results of spontaneous word list generation test (Pearson r= −0.66, p b 0.00), 15-WLRT (Pearson r = −0.56, p b 0.00), TMT-A (Pearson r= 0.61, p b 0.00), TMT-B (Spearman r = 0.83, p b 0.00), visual and auditory digit span (Pearson r = −0.72, p b 0.00 and Pearson −0.56, p b 0.00, respectively), attention to detail
3.3.1. Neurotrophin-3 No statistically significant relationship between NT-3 PBMC concentration and neuropsychological tests results has been found (see Table 4). 3.3.2. Beta-NGF A) Beta-NGF PBMC concentration did not correlate with the following neuropsychological tests results: TMT-A, TMT-B, visual and auditory digit span, attention to detail, and Raven's Coloured Progressive Matrices Test (see Table 4). B) Beta-NGF PBMC concentration correlated significantly with spontaneous word list generation test result (Pearson r = 0.37, p = 0.02, see Fig. 1A). This relationship was further identified in a linear regression analysis model, characterized by the following performance measures: R 2 = 0.14, p = 0.02.
Table 3 Descriptive statistics for neuropsychological tests results in RRMS patients. Median Spontaneous word list generation tests 15-Word List Recall Test(15-WLRT) TMT-A [ms] TMT-B [ms]
20
Visual digit span test Auditory digit span test Attention to detail Raven's Coloured Progressive Matrices Pattern recognition
7 6 7 33
5 27.8 66.8
Q1-Q3 (IQR)
Range
Examined cognitive functions
17–23
12–31
Semantic verbal fluency, associative thinking (clustering), switching, abstractive reasoning
4–6 21.6–47.0 44.6–91.1
2–11 12.0–15.4 35.3–426.1
6–9 5–7 4–8 30–35
4–12 5–10 1–8 20–36
Verbal memory Psychomotor speed, the level of concentration, attention, visuo-spatial searching Visuo-spatial working memory, ability to switch to a new criterion after learning a specific reaction principle Visual working memory capacity Auditory working memory capacity Visual memory, attention Logical reasoning (general intelligence, g)
Correct answers in 20 patients (49%) Incorrect answers in 21 patients (51%)
Visual memory
Since variables are either ordinal or non-normally distributed, median, interquartile range (IQR) and range are provided. Q1 — first quartile, Q3 — third quartile. The last column contains brief characterization of cognitive functions tested by the tests listed in the first column.
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However, this relationship did not reach statistical significance in a stepwise regression model for word list generation test performance as a dependent variable, including such other covariates as age and total demeylinating plaque volume (both significant), with disease duration, education level and BPF being non-significant. C) Beta-NGF PBMC concentration correlated significantly with 15-WRLT result (Pearson r = 0.4, p b 0.00, see Fig. 1B), which was also identified in a linear regression analysis model (R2 = 0.16, p = 0.01). Although performance in this test is strongly dependent on education level (Spearman r= 0.40, p b 0.00), in stepwise regression model analysis for 15-WLRT result as a depending variable this strong relationship did not reach statistical significance, on contrary to relationship with PBMC betaNGF, which remained statistically significant. The performance measures for this model are as follows: adjusted R2 = 0.42, p b 0.00; significant covariates include PBMC beta-NGF concentration (R= 0.4, p b 0.00) and age (R= −0.55, p b 0.00), nonsignificant covariates include years of education, BPF and total demyelinating plaque volume. Association between different neurotrophins' concentrations and cognitive performance in the study group are illustrated in Fig. 2. 3.4. Correlation between cognitive dysfunction, neuroradiological and clinical parameters Most of the neuropsychological tests' results correlated significantly with brain atrophy, total lesion volume, and EDSS score, as presented in Table 5. 4. Discussion In MS neurotrophins can activate several signaling pathways related to neuroprotection. They may exert their effects directly by acting on neuronal trk receptors, promoting their survival, or indirectly by interactions with trk receptors on different subpopulations of immune cells [26,27]. In this study we addressed the question whether neurotrophic status may be protective of cognitive decline in MS patients. We have found that PBMC-derived beta-NGF concentration is significantly positively correlated with spontaneous word list generation and 15-WLRT results in RRMS patients. The results of both these tests, assessing semantic and working memory, correlated strongly with the
Fig. 2. Association of NT-3 and beta-NGF PBMCs concentrations and cognitive performance in RRMS patients. Point size reflects the number of neuropsychological tests classified below the group median (cognitive composite score). PBMCs — peripheral blood mononuclear cells.
global cognitive deficit in the investigated group, as defined in our study. The relationship with word list generation test result, although identified by means of correlation, did not reach statistical significance in the stepwise regression model, being dominated by the strong impact of education and total lesion volume. On contrary, in stepwise regression model the impact of immune-cell derived beta-NGF on 15-WLRT result was shown to be stronger than the one of education level or radiological markers of MS-related damage. The fact that in our model the influence of education on 15-WLRT result is lost is obviously surprising. One possible explanation could be that in our study cohort education is strongly negatively correlated with age (Spearman r= −0.35, p = 0.02), namely young adults are better educated than the older ones. Given the strong association of 15-WLRT with age, it may be expected that the weaker associate, which in this case is education, would be deleted from the model. To summarize, we have shown for the first time that immune-cell beta-NGF expression may be protective of cognitive deficit in RRMS patients. This is in line with evidence obtained from animal models (see: Introduction) and with generally accepted fact that beta-NGF
Fig. 1. Scatterplots illustrating the relationship between PBMCs beta-NGF concentration and: spontaneous word list generation (WLG) test result (A) and 15-Word List Recall test (15-WLRT) result (B). Least square regression line shows the degree of association between the two variables. Correlation A is described by Pearson correlation coefficient r = 0.37 (p = 0.02). Correlation B is described by Pearson correlation coefficient r = 0.40 (p = 0.00).
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Table 5 Correlation matrices for neuropsychological tests results, neuroradiological and clinical parameters in the investigated group. Pearson or Spearman* correlation coefficient r (p)
Spontaneous word list generation tests 15-Word List Recall Test TMT-A [ms] TMT-B [ms] Visual digit span test Auditory digit span test Attention to detail Raven's Coloured Progressive Matrices (total score) RT Q1 RT median RT IQR RT Q3
BPF
Total lesion volume (cm3)
Corpus callosum cross-sectional area (mm2)
EDSS
r = 0.40 p b 0.00 r = 0.21 p = 0.18 r = −0.61 p b 0.00 *r = −0.62 p b 0.00 r = 0.58 p b 0.00 r = 0.56 p = 0.001 *r = 0.65 p b 0.00 r = 0.59 p b 0.00
*r= −0.49 p b 0.00 *r = −0.25 p = 0.11 *r= 0.43 p b 0.00 *r= 0.58 p b 0.00 *r= −0.46 p = 0.003 *r= −0.45 p = 0.003 *r= −0.48 p = 0.001 *r= −0.47 p = 0.002
r = 0.45 p b 0.00 r = 0.07 p = 0.66 r = −0.59 p b 0.00 *r = −0.59 p b 0.00 r = 0.50 p b 0.00 *r = 0.53 p b 0.000 *r = 0.45 p = 0.003 r = 0.49 p = 0.001
*r = −0.51 p b 0.00 *r = −0.52 p b 0.00 *r = 0.54 pb 0.00 *r = 0.65 pb 0.00 *r = −0.42 p b 0.00 *r = −0.29 p = 0.07 *r = −0.32 p = 0.04 *r = −0.51 p b 0.00
r = −0.23 r = −0.29 r = −0.08 r = −0.29
p = 0.16 p = 0.08 p = 0.63 p = 0.07
*r = 0.15 *r = 0.16 *r = 0.11 *r = 0.20
p = 0.37 p = 0.35 p = 0.50 p = 0.22
r = −0.11 p = 0.52 r = −0.13 p = 0.45 r = 0.02 p = 0.91 r = −0.14 p = 0.4
*r = 0.14 *r = 0.15 *r = 0.25 *r = 0.18
p = 0.41 p = 0.36 p = 0.13 p = 0.26
For normally or non-normally distributed variables Pearson or Spearman rank (marked with * sign) correlation coefficients were calculated, respectively. Statistically significant values are marked with bold type. IQR — interquartile range, Q1 — first quartile, Q3 — third quartile, TMT — trail making test, RT — reaction time.
is one of the factors facilitating survival and proper function of cholinergic neurons in the adult brain. It may be speculated that in MS, given a high level of neuronal destruction, there is also a higher demand for beta-NGF. In 2005 D'Intino et al. [11] showed that in Lewis rats with EAE memory and learning deficits correlated with lower activity of acetylcholinesterase and with lower beta-NGF mRNA expression in cortex, hippocampus and basal forebrain. Moreover, the use of selective acetylcholinesterase inhibitors (AChEI; rivastigmine and donepezil) improved cognitive functions and restored the proper acetylcholinesterase activity and beta-NGF expression. In this context our findings indirectly support the concept of treating MS-related cognitive deficit with AChEI. Although promising results were provided in a single-center randomized trial on donepezil use for this indication [28], larger multicenter trial failed to confirm its efficacy on memory impairment in MS patients [29]. Undoubtedly, future studies with other AChEI are needed. It is worth mentioning that NGF-mediated acetylocholine release is dependent upon extracellular choline availability [30]. Interestingly, choline concentration measured by 1H MR spectroscopy is lower in MS patients with no demeylination found in the routine MRI studies [31], which might mean it is being efficiently used for the intracellular acetylocholine synthesis. Such patients could constitute a distinct group with a higher neuroprotective potential. Different choline availability in different MS patients may influence the neuroprotective effect of beta-NGF and thus the correlation between cognitive variables and beta-NGF expression might not be straightforward. The next step in our research would be to combine immune cell-derived beta-NGF level measurements with neuropsychological and spectroscopy data in the same group of RRMS patients. Early identification of cognitive deficit in MS patients is crucial in the context of their life quality, giving possibility for an early introduction of psychotherapy and potential pharmacotherapy. Broad neuropsychological examination is time-consuming and cannot be recommended as a standard procedure in assessing MS patients. The widely used Paced Auditory Serial Addition Test is time-consuming, complicated and demanding for both, patients and doctors, and the learning effect has been shown [32]. We suggest that the simple, easily accessible tests — that in our study correlated with global cognitive deficit — might be used as screening markers for a generalized cognitive dysfunction in MS, namely: visual digit span, spontaneous word list generation tests, 15-WLRT, and attention to detail test. All these tests depend on the function of attention. Obviously, implementation of these tests into everyday practice requires validation. However, for now they can serve as useful tools in assessing the subtle interrelationships between cognitive, neuroradiological and neuroimmunological parameters in MS patients.
A possible limitation of our current study may be associated with the fact that cognitive functions decline slowly over time, while beta-NGF production by immune cells varies significantly during disease course, thus their interrelationship is expected to be non-linear and complex. Follow-up studies for both, neuroimmunological and neuropsychological parameters, are planned for the investigated group. It would also be interesting to investigate if any other neuroimmunological markers, such as inflammatory cytokines and chemokines, correlate with the level of cognitive dysfunction in MS patients. It seems noteworthy that while in our previous study [14] we showed that NT-3 and no other neurotrophin correlated with brain atrophy measures, in the same group of patients we have now identified a positive correlation between beta-NGF and semantic memory, which has not been observed for NT-3 or other neurotrophins. One of the possible explanations could be that different neurotrophins exert different biological roles, acting primarily on different populations of neurons. While beta-NGF has been linked to development and maintenance of the cholinergic phenotype of the basal forebrain and hippocampal neurons, such correlation was not noted for other neurotrophins, except for BDNF, which we did not analyze in this study. Also, NGF has been shown to prevent the loss of synaptic connections, thus facilitating synaptic plasticity [33]. Influence on the cholinergic innervation and modulation of activity dependent synaptic plasticity could account for the fact that it is NGF, and not other neurotrophins, that is linked to cognitive performance. On the other hand, NT-3 was shown to increase oligodendrocyte proliferation and differentiation of oligodendrocyte precursor cells [34], which facilitates neuroregeneration. Neuroregeneration can prevent brain atrophy accumulation. This may account for NT-3 correlation with brain atrophy measures. NT-3 seems more potent as a mediator of neuroregeneration, while NGF rather mediates neuroprotection, especially of the cholinergic phenotype. Also, in contrast to NGF, NT-3 is expressed mainly in the developing and not mature brain [35]. Therefore, even small reduction of adult baseline NT-3 levels can lead to significant functional disturbances that could be revealed also in a small cohort of patients. It might be that our sample size was too small to demonstrate all correlations, while revealing more prevalent ones. Another observation that seems noteworthy is that we detected NT-4/5 in nearly 80% of sera, but only in 25% of PBMCs samples, whereas for the other two neurotrophins our detection rate was the opposite. Based on the available literature, NT-4/5 seems to be the least potent of the neurotrophins we investigated. It can be hypothesized that its higher serum level is due to lower uptake within the damaged CNS, where more potent neurotrophins are used preferably. Another speculation could be that the peripheral source of NT-4/5 is different than PBMCs. NT-4/5 is ubiquitously expressed within the immune system, so different cell lines, including granulocytes and
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bone marrow cells, and not PBMCs, can provide additional secretion of NT-4/5. Our findings are preliminary; however, they do provide rationale for targeting NGF-pathway as a novel therapeutic strategy in MS. So far application of recombinant NGF has not been successful due to short half-life in vivo, difficulties crossing the blood–brain barrier, proteolytic degradation and potential side-effects [36]. NGF gene therapy is another option, already being tested for Alzheimer disease [37]. Small-molecule agonists for trk receptors would be yet another alternative. Interestingly, it has been shown that interferon beta, which is one of the currently approved and widely used immunomodulating agents for MS treatment, promotes NGF secretion early in the course of MS [38]. Such neuroprotective effect could at least partially contribute to interferon efficacy in MS. Immunomodulating and immunosuppressive therapies are only partially effective in MS. It may be that one should focus on implementing regenerating strategies instead. These would facilitate early and effective remyelination, thus preventing axonal degeneration. Targeting NGF-pathway may be an attractive option, especially in the context of MS-related cognitive deficit. Acknowledgements and conflict of interest All authors have nothing to disclose. M.A. Pawlak is funded by National Science Centre grant 2011/01/D/NZ4/05801. References [1] Lassman H. What drives disease in multiple sclerosis: inflammation or neurodegeneration? Clin Exp Neuroimmunol 2010;1:2–11. [2] Rao SM, Leo GJ, Bernardin L, Unverzagt F. Cognitive dysfunction in multiple sclerosis. Frequency patterns, and prediction. Neurology 1991;41:685-91. [3] Ron MA, Callanan MM, Warrington EK. Cognitive abnormalities in multiple sclerosis: a psychometric and MRI study. Psychol Med 1991;21:59-68. [4] Kujala P, Portin R, Ruutiainem J. Memory deficits and early cognitive deterioration in MS. Acta Neurol Scand 1996;93:329-35. [5] Piras MR, Magnano I, Canu EDG, Paulus KS, Satta WM, Soddu A, et al. Longitudinal study of cognitive dysfunction in multiple sclerosis: neuropsychological, neuroradiological and neurophysiological findings. J Neurol Neurosurg Psychiatry 2003;74:878-85. [6] Lewin GR, Barde Y-A. Physiology of the neurotrophins. Annu Rev Neurosci 1996;19:289-317. [7] Connor B, Dragunow M. The role of neuronal growth factors in neurodegenerative disorders of the human brain. Brain Res Rev 2009;27:1–39. [8] Chen KS, Nishimura MC, Armanini MP, Crowley C, Spencer SD, Phillips HS. Disruption of a single allele of the nerve growth factor gene results in atrophy of basal forebrain cholinergic neurons and memory deficits. J Neurosci 1997;17:7288-96. [9] Korte M, Carroll P, Wolf E, Brem G, Thoenen H, Bonhoeffer T. Hippocampal long-term potentiation is impaired in mice lacking brain-derived neurotrophic factor. Proc Natl Acad Sci U S A 1995;92:8856-60. [10] Mo L, Yang Z, Zhang A, Li X. The repair of the injured adult rat hippocampus with NT-3-chitosan carriers. Biomaterials 2010;31:2184-92. [11] D'Intino G, Paradisi M, Fernandez M, Giuliani A, Aloe L, Giardino L, et al. Cognitive deficit associated with cholinergic and nerve growth factor down-regulation in experimental allergic encephalomyelitis in rats. PNAS 2005;102:3070-5. [12] Patanella AK, Zinno M, Quaranta D, Nociti V, Frisullo G, Gainotti G, et al. Correlations between peripheral blood mononuclear cell production of BDNF, TNF-alpha, IL-6, IL-10 and cognitive performances in multiple sclerosis patients. J Neurosci Res 2010;88:1106-12. [13] Weinstock-Guttman B, Benedict RH, Tamaño-Blanco M, Ramasamy DP, Stosic M, Polito J, et al. The rs2030324 SNP of brain-derived neurotrophic factor (BDNF) is associated with visual cognitive processing in multiple sclerosis. Pathophysiology 2011;18:43-52.
[14] Kalinowska-Łyszczarz A, Pawlak MA, Michalak S, Paprzycki W, Losy J. Immune cell NT-3 expression is associated with brain atrophy in multiple sclerosis patients. J Neuroimmunol 2011;240–241:109-13. [15] Polman CH, Reingold SC, Edan G, Filippi M, Hartung HP, Kappos L, et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria”. Ann Neurol 2005;58:840-6. [16] Lowry OH, Rosebrough NJ, Farr AL, Randall RJ. Protein measurement with the Folin phenol reagent. J Biol Chem 1951;193:265-75. [17] Huijbregts SCJ, Kalkers NF, de Sonneville LMJ, de Groot V, Reuling LEW, Polman CH. Differences in cognitive impairment of relapsing remitting, secondary, and primary progressive MS. Neurology 2004;63:335-9. [18] Spreen O, Strauss E. A compendium of neuropsychological tests. 2nd ed. New York, Oxford: Oxford University Press; 1998. [19] Mueller ST. PEBL: The psychology experiment building language (Version 0.10) [Computer experiment programming language]. Retrieved Nov. 2009 from http:// pebl.sourceforge.net 2009. [20] Carter P. The complete book of intelligence tests. The Atrium, Southern Gate, Chichester, West Sussex, England: John Wiley and Sons Ltd; 2005. [21] Broshek DK, Barth JT. The Halstead–Reitan neuropsychological test battery. In: Groth-Marnat G, editor. W: neuropsychological assessment in clinical practice: a guide to test interpretation and integration, red. New York: John Wiley and Sons; 2000. [22] Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 1971;9:97–113. [23] Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An inventory for measuring depression. Arch Gen Psychiatry 1961;4:53-63. [24] Jaracz K, Wołowicka L, Baczyk G. Validation analysis of the Polish version of the Ferrans and Powers Quality of Life Index. Post Rehabil 2001;4:67-73. [25] Lilliefors H. On the Kolmogorov–Smirnov test for normality with mean and variance unknown. J Am Stat Assoc 1967;62:399-402. [26] Tabakman R, Lecht S, Sephanova S, Arien-Zakay H, Lazarovici P. Interactions between the cells of the immune and nervous system: neurotrophins as neuroprotection mediators in CNS injury. Prog Brain Res 2004;146:387-401. [27] Linker R, Lee D-H, Siglienti I, Gold R. Is there a role for neurotrophins in the pathology of multiple sclerosis? J Neurol 2007;254(Suppl. 1):I/33-40. [28] Krupp LB, Christodoulou C, Melville P, Scherl WF, MacAllister WS, Elkins LE. Donepezil improved memory in multiple sclerosis in a randomized clinical trial. Neurology 2004;63:1579-85. [29] Krupp LB, Christodoulou C, Melville P, Scherl WF, Pai LY, Muenz LR, et al. Multicenter randomized clinical trial of donepezil for memory impairment in multiple sclerosis. Neurology 2011;76(17):1500-7. [30] Auld DS, Mennicken F, Day JC, Quirion R. Neurotrophins differentially enhance acetylcholine release, acetylcholine content and choline acetyltransferase activity in basal forebrain neurons. J Neurochem 2001;77:253-62. [31] Gustafsson MC, Dahlqvist O, Jaworski J, Lundberg P, Landtblom AM. Low choline concentrations in normal-appearing white matter of patients with multiple sclerosis and normal MR imaging brain scans. AJNR Am J Neuroradiol 2007;28: 1306-12. [32] Rosti-Otajärvi E, Hämäläinen P, Koivisto K, Hokkanen L. The reliability of the MSFC and its components. Acta Neurol Scand 2008;117:421-7. [33] Purves D, Njå A. Effect of nerve growth factor on synaptic depression after axotomy. Nature 1976;260:535-6. [34] Grill R, Murai K, Blesch A, Gage FH, Tuszynski MH. Cellular delivery of neurotrophin-3 promotes corticospinal axonal growth and partial functional recovery after spinal cord injury. J Neurosci 1997;17:5560-72. [35] Ernfors P, Ibáñez CF, Ebendal T, Olson L, Persson H. Molecular cloning and neurotrophic activities of a protein with structural similarities to nerve growth factor: developmental and topographical expression in the brain. Proc Natl Acad Sci U S A 1990;87:5454-8. [36] Colafrancesco V, Villoslada P. Targeting NGF pathway for developing neuroprotective therapies for multiple sclerosis and other neurological diseases. Arch Ital Biol 2011;149:183-92. [37] Blesch A. Neurotrophin gene therapy for Alzheimer's disease. Future Neurol 2006;1:179-87. [38] Biernacki K, Antel JP, Blain M, Narayanan S, Arnold DL, Prat A. Interferon beta promotes nerve growth factor secretion early in the course of multiple sclerosis. Arch Neurol 2005;62(4):563-8.
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Journal of the Neurological Sciences journal homepage: www.elsevier.com/locate/jns
Cognitive deficit is related to immune-cell beta-NGF in multiple sclerosis patients Alicja Kalinowska-Łyszczarz a, b,⁎, Mikołaj A. Pawlak c, Sławomir Michalak a, d, Jacek Losy d, e a
Department of Neurochemistry and Neuropathology, Chair of Neurology, Poznan University of Medical Sciences, Poland Heliodor Swiecicki University Hospital, Poznan, Poland c Department of Neurology and Cerebrovascular Disorders, Poznan University of Medical Sciences, Poland d Neuroimmunological Unit, Institute of Experimental and Clinical Medicine, Polish Academy of Sciences, Poland e Department of Clinical Neuroimmunology, Chair of Neurology, Poznan University of Medical Sciences, Poland b
a r t i c l e
i n f o
Article history: Received 1 March 2012 Received in revised form 29 May 2012 Accepted 19 July 2012 Available online 9 August 2012 Keywords: Multiple sclerosis Cognitive dysfunction Neuroimmunology Neurotrophic factors Neurotrophins Neuropsychology
a b s t r a c t Objectives: Multiple sclerosis (MS) is a chronic inflammatory demyelinating and neurodegenerative disease of the central nervous system (CNS), causing cognitive impairment in 45–65% of patients. Beta-NGF facilitates proper cholinergic transmission in the healthy CNS. In MS-damaged tissue there is a relative deficit of neurotrophins that might be compensated by peripheral blood mononuclear cells (PBMCs) synthesis. Our aim was to evaluate the relationship between PBMCs neurotrophins' expression and cognitive performance in relapsing–remitting MS (RRMS) patients. Patients and methods: Beta-NGF, NT-3 and NT-4/5 levels were measured in sera and in PBMCs by ELISA method in 41 RRMS patients in remission. All patients underwent neuropsychological assessment with a battery of 10 tests evaluating a wide range of cognitive functions. Results: PBMCs beta-NGF concentration correlated significantly with spontaneous word list generation test (Pearson R = 0.37, p = 0.02) and 15-Word List Recall Test results (Pearson R = 0.40, p b 0.00). Both tests assessing semantic memory correlated significantly with the cognitive composite score, defined as a number of tests in which patients performed below group median for the given test. Conclusions: In RRMS beta-NGF is strongly linked to cognitive performance, which makes it an attractive therapeutic target. It might play a neuroprotective role in MS, especially in the cognitive domain. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Multiple sclerosis (MS) has originally been considered a chronic inflammatory demyelinating disease of the central nervous system (CNS), affecting primarily the white matter of the brain and spinal cord, and leading to inevitable accumulation of disability in patients. The original definition has changed over years and it is now established that while driven by autoimmune inflammatory reaction, the disease also develops a neurodegenerative component [1]. Autoimmune reaction may in fact play a protective role in the CNS of MS patients. CD4+ Th2 cells produce anti-inflammatory cytokines, and peripheral blood mononuclear cells (PBMCs) secrete neurotrophic factors, including neurotrophins, namely brain-derived neurotrophic factor (BDNF), beta nerve growth factor (beta-NGF), neurotrophin 3 (NT-3) and neurotrophin 4/5 (NT-4/5). In MS additional support from PBMCs might compensate the relative neurotrophic deficiency in the damaged CNS tissue. Failure to produce the adequate neurotrophic factors' concentrations might result in impaired regeneration within the CNS, ⁎ Corresponding author at: Department of Neurochemistry and Neuropathology, Chair of Neurology, Poznan University of Medical Sciences, ul. Przybyszewskiego 49, 60‐355 Poznan, Poland. Tel.: +48 61 869 1454; fax: +48 61 869 1697. E-mail address: [email protected] (A. Kalinowska-Łyszczarz). 0022-510X/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jns.2012.07.044
consequently leading to more pronounced atrophy, which is the main determinant of MS patients' end-point disability. Cognitive dysfunction occurs in approximately 45 to 65% of MS patients [2,3], independently of disease course, duration, clinical disability or total demyelinating lesion volume measured in magnetic resonance imaging (MRI) studies. Cognitive functions that are typically impaired in MS patients include: attention, visuo-spatial analysis, abstract reasoning, multi-tasking, operative and semantic memory, and information processing speed [3,4]. Such pattern is consistent with disconnection syndrome that is observed in disruption of white matter pathways similar to the pattern occurring in subcortical dementia [5]. In the adult brain neurotrophins facilitate synaptic plasticity, which is crucial for memory and other cognitive domains. BDNF, NGF, NT-3 and their receptors, tropomyosin related kinase B (TrkB) and TrkC, are expressed at relatively high levels in the adult hippocampus [6]. NGF has been shown to exert a protective effect on the cholinergic system in animal models [6–8]. BDNF deficiency may lead to reduction in hippocampal synaptic plasticity [9], and exogenous NT-3 supply improves cognitive skills in rats [10]. MS-associated cognitive dysfunction may thus be related to inadequate neurotrophin production within the CNS. This seems especially plausible in light of animal models research findings, i.e. the fact that cognitive deficit has been associated with NGF-dependent cholinergic deficiency in cerebral cortex, hippocampus
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and basal forebrain of rats with experimental allergic encephalomyelitis, which is an animal model for human MS [11]. So far there have only been a few studies showing the potential link between neurotrophins and cognitive functions in MS patients, all with regard to BDNF only [12,13]. Since neurotrophins have been implicated in memory acquisition and retention, we hypothesize that they exert neuroprotective role in MS, especially in the cognitive domain. We have previously shown that higher immune cell NT-3 expression is associated with reduced brain atrophy in relapsing–remitting MS (RRMS) patients, as measured by brain-parenchymal fraction (BPF) and corpus callosum crosssectional area in MRI studies [14]. In this paper, on the other hand, we present data suggesting that PBMCs expression of another neurotrophin, namely beta-NGF, is associated with better performance in neuropsychological tests in RRMS patients. 2. Patients and methods 2.1. Patient population Fourty-one patients, including 25 females and 16 males, with clinically definite RRMS, according to revised (2005) McDonald criteria [15], were recruited for the study and written informed consent was obtained from all the participants. The study protocol was approved by the Internal Review Board at Poznan University of Medical Sciences. Exclusion criteria included: current relapse stage or relapse within the last 8 weeks, steroid treatment within the last 3 months, immunomodulatory therapy in the last year, age older than 65 years, concomitant psychiatric disease, current use of antidepressants, neuroleptics or antiepileptic drugs, alcohol or drug abuse, diagnosis of another autoimmune or neoplastic pathology, relevant motor deficit in the dominant hand, upper limb ataxia or loss of visual acuity, dementia or mental impairment. All patients underwent 1.5 T MRI with the following sequences: Magnetisation Prepared Rapid Acquisition Gradient Echo (MPRAGE), FLAIR space and diffusion tensor imaging, as described in our previous manuscript [14]. Brain-parenchymal fraction (BPF) and corpus callosum cross-sectional area (CCA) were calculated according to the methodology published previously [14]. Clinical characteristics of the examined group, including data from image analysis, are presented in Table 1. 2.2. Laboratory protocol Blood was taken from patients to obtain sera and PBMCs samples. PBMCs were isolated using Ficoll–Paque (GE Healthcare) gradient method. Samples were kept frozen at −80 °C until further analysis. Before measurement of neurotrophin concentrations, PBMCs were resuspended in a lysis buffer (NaCl, Tris–HCl, EDTA), supplemented
with Triton X-100 (Sigma-Aldrich) and a proteinase inhibitor cocktail (Sigma-Aldrich). The protein content was determined using the Lowry method [16]. Beta-NGF, NT-3 and NT-4/5 levels were measured in sera and PBMCs by ELISA method, according to manufacturer's instructions (RayBio Human Beta-NGF ELISA Kit, RayBiotech, RayBio Human NT-4 ELISA Kit, RayBiotech, ChemiKine NT-3 Sandwich ELISA kit, Millipore). In PBMCs their concentrations were expressed as relevant weight units per one milligram of the protein (pg/mg protein for NT-3 and fg/mg protein for beta-NGF). The detection limits for beta-NGF, NT-3 and NT-4/5 were less than 14.0, 7.8 and 2.0 pg/ml, respectively.
2.3. Neuropsychological tests battery After full neurological examination, including EDSS (Expanded Disability Status Scale) scoring, all patients were subject to neuropsychological examination, which included 10 following tests: A. spontaneous word list generation test, which is a modified version of phonemic naming task from Brief Repeatable Battery of Neuropsychological Tests [17], B. 15-Word List Recall Test (15-WLRT), which is a modification of Rey Auditory Verbal Learning Test and California Verbal Learning Test, and comprises memorizing a list of 15 unrelated words, as in the Rey test, but in the visual modality, in a single trial and with no distractors [18], C. simple reaction time (RT) test, created with the use of PEBL software [19], D. pattern recognition test [20], E. attention to detail test [20], F. Raven's Coloured Progressive Matrices [18], G. Trail Making Tests (TMT), versions A and B [21], and H. visual digit span forward and auditory digit span forward. A short characterization of the cognitive functions evaluated by the tests is depicted in Table 3, alongside descriptive statistics of patients' results. Patients were examined by the same clinician, under similar conditions (room, daytime, and lighting) and using identical test order. Factors that could potentially influence patient's performance (i.e. headache after lumbar puncture, tiredness, and other) were excluded before examination. Time interval between blood sampling, neuropsychological and neurological assessment was within 5 days. Handedness was assessed according to Edinburgh Handedness Inventory [22], severe depression was ruled out with the use of Beck Depression Inventory [23], and the quality of life was assessed with the Polish adaptation of Ferrans and Powers Quality of Life Index [24]. Level of education was described as a number of education years. We defined results obtained in each neuropsychological (NP) test as deficient, when they were worse than the median for the given test. Therefore, the cognitive composite score had a range from 0 (all NP test results above median) to 10 (all NP test results below median).
Table 1 Descriptive statistics for clinical data of the examined RRMS group.
Age (years) Disease duration (years) EDSS BPF Total white matter lesion volume (cm3) Corpus callosum cross-sectional area (CCA, mm²) Education (years)
Mean ± SD or *Median (IQR) n = 41
Range
39 ± 11 *3 (0.65–10) *1.5 (0.25–2.38) 0.77 ± 0.03 *3.36 (0.92–13.56)
18–63 0.08–24 0–8.5 0.706–0.809 0.15–57.14
606.63 ± 134.16
318.75–862.5
*12 [10–17]
8–17
For normally distributed variables mean, standard deviation and range are provided. For ordinal variables and non-normally distributed continuous variables median, interquartile range (IQR) and range are provided — these are preceded by * mark.
2.4. Statistical analysis Analysis was performed in R CRAN statistical environment (www. r-project.org; R Development Core Team, R Foundation for Statistical Computing, 2010). Distribution of variables was evaluated using Lilliefors test [25]. Summary statistics for continuous normally distributed variables was calculated as mean, standard deviation and range. For ordinal variables and non-normally distributed continuous variables median, first and third quartiles and range were calculated. Spearman rank correlation coefficients were calculated to assess the association between neuropsychological performance results and neurotrophin concentrations. Multiple regression models were developed using clinical data, neurotrophin concentrations and neuropsychological data.
A. Kalinowska-Łyszczarz et al. / Journal of the Neurological Sciences 321 (2012) 43–48 Table 2 NT-3 and beta-NGF detection in PBMCs samples from RRMS patients. Neurotrophin
mean ± SD
Range
Detection n (%)
PBMC NT-3 (pg/mg protein) PBMC beta-NGF (pg/mg protein)
590.1 ± 327.52 1.26 ± 0.8
55.72–1710.39 0.33–3.74
38 (93%) 41 (100%)
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Table 4 Correlation matrices for PBMC NT-3 and beta-NGF concentrations and neuropsychological tests results. Pearson or Spearman* correlation coefficient r (p) NT-3 PBMC (pg/mg protein)
Since variables are distributed normally, mean, standard deviation (SD) and range are provided.
3. Results 3.1. Neurotrophin expression in sera and in PBMCs Beta-NGF, NT-3 and NT-4/5 were detected in 25 (61%), 15 (36.6%) and 32 (78.1%) of the 41 sera samples, respectively. In PBMCs beta-NGF was detected in all the samples, with the median concentration 1.07 pg/mg protein; NT-3 was found in 93% of samples, with the median concentration of 557.8 pg/mg protein; and NT-4/5 PBMCs detection was only 24.4%. Further statistical analysis was performed only when neutrophin detection was at the level of at least 80% for the given fraction. The summary of relevant data, namely beta-NGF and NT-3 expression in PBMCs, is presented in Table 2. Outliers were corrected with Grubbs test. As we have shown previously, NT-3 and beta-NGF levels did not correlate with patient age, gender and EDSS score, and beta-NGF concentration decreased with disease duration [14]. NT-3 and beta-NGF were positively correlated (Spearman correlation coeficcient r= 0.38, p= 0.02).
Spontaneous word list generation tests 15-Word List Recall Test TMT-A [ms] TMT-B [ms] Visual digit span Auditory digit span Attention to detail Raven's Coloured Progressive Matrices RT Q1 RT median RT IQR RT Q3
r = 0.09 (p = 0.60) r = − 0.16 (p = 0.34) r = − 0.17 (p = 0.30) *r = − 0.03 (p = 0.85) *r = 0.07 (p = 0.65) *r = 0.07 (p = 0.69) *r = 0.12 (p = 0.48) r = 0.21 (p = 0.20) r = 0.73 r = 0.04 r = 0.24 r = 0.00
(p = 0.67) (p = 0.81) (p = 0.16) (p = 0.98)
beta-NGF (pg/mg protein) r = 0.37 (p = 0.02) r = 0.40 (p = 0.00) r = − 0.12 (p = 0.45) *r = − 0.16 (p = 0.31) *r = 0.05 (p = 0.76) *r = −0.06 (p = 0.72) *r = 0.24 (p = 0.14) r = 0.19 (p = 0.23) r = 0.02 (p = 0.89) r = 0.05 (p = 0.75) r = 0.08 (p = 0.64) r = − 0.01 (p = 0.94)
For normally or non-normally distributed variables Pearson or Spearman rank correlation coefficient were calculated, respectively. Statistically significant correlation is marked with bold type. TMT — trail making test, RT — reaction time test, Q1 — first quartile, Q3 — third quartile, IQR — interquartile range.
(Spearman r = −0.77, p b 0.00), and Raven's Coloured Progressive Matrices Test (Pearson r = −0.68, p b 0.00). However, in stepwise regression model for the cognitive composite score as a dependent variable only the relationships with the following tests reached statistical significance as covariates: visual digit span, spontaneous word list generation tests, 15-WRLT, and attention to detail. This model is described by R² = 0.84 (p b 0.00). 3.3. Correlation between cognitive dysfunction and PBMC neurotrophin expression
3.2. Cognitive performance Descriptive statistics for neuropsychological test performance of the investigated RRMS group is presented in Table 3. The median results for different RT distribution parameters were as follows: RT min = 85 ms, RT Q1 (first quartile) = 309.5 ms, RT median=339.5 ms, RT Q3 (third quartile)=396.25 ms, IQR=264 ms. In the investigated group the correlation coefficient between Q1 and Q3 for RT test was r=0.87 (pb 0.00). The cognitive composite score achieved by the patients, expressed as the number of cognitive tests in which their result was below median for the given test, correlated significantly with RT distribution parameters, namely median (Pearson r = 0.33, p = 0.05) and Q3 (Pearson r= 0.35, p = 0.03), and with the results of spontaneous word list generation test (Pearson r= −0.66, p b 0.00), 15-WLRT (Pearson r = −0.56, p b 0.00), TMT-A (Pearson r= 0.61, p b 0.00), TMT-B (Spearman r = 0.83, p b 0.00), visual and auditory digit span (Pearson r = −0.72, p b 0.00 and Pearson −0.56, p b 0.00, respectively), attention to detail
3.3.1. Neurotrophin-3 No statistically significant relationship between NT-3 PBMC concentration and neuropsychological tests results has been found (see Table 4). 3.3.2. Beta-NGF A) Beta-NGF PBMC concentration did not correlate with the following neuropsychological tests results: TMT-A, TMT-B, visual and auditory digit span, attention to detail, and Raven's Coloured Progressive Matrices Test (see Table 4). B) Beta-NGF PBMC concentration correlated significantly with spontaneous word list generation test result (Pearson r = 0.37, p = 0.02, see Fig. 1A). This relationship was further identified in a linear regression analysis model, characterized by the following performance measures: R 2 = 0.14, p = 0.02.
Table 3 Descriptive statistics for neuropsychological tests results in RRMS patients. Median Spontaneous word list generation tests 15-Word List Recall Test(15-WLRT) TMT-A [ms] TMT-B [ms]
20
Visual digit span test Auditory digit span test Attention to detail Raven's Coloured Progressive Matrices Pattern recognition
7 6 7 33
5 27.8 66.8
Q1-Q3 (IQR)
Range
Examined cognitive functions
17–23
12–31
Semantic verbal fluency, associative thinking (clustering), switching, abstractive reasoning
4–6 21.6–47.0 44.6–91.1
2–11 12.0–15.4 35.3–426.1
6–9 5–7 4–8 30–35
4–12 5–10 1–8 20–36
Verbal memory Psychomotor speed, the level of concentration, attention, visuo-spatial searching Visuo-spatial working memory, ability to switch to a new criterion after learning a specific reaction principle Visual working memory capacity Auditory working memory capacity Visual memory, attention Logical reasoning (general intelligence, g)
Correct answers in 20 patients (49%) Incorrect answers in 21 patients (51%)
Visual memory
Since variables are either ordinal or non-normally distributed, median, interquartile range (IQR) and range are provided. Q1 — first quartile, Q3 — third quartile. The last column contains brief characterization of cognitive functions tested by the tests listed in the first column.
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However, this relationship did not reach statistical significance in a stepwise regression model for word list generation test performance as a dependent variable, including such other covariates as age and total demeylinating plaque volume (both significant), with disease duration, education level and BPF being non-significant. C) Beta-NGF PBMC concentration correlated significantly with 15-WRLT result (Pearson r = 0.4, p b 0.00, see Fig. 1B), which was also identified in a linear regression analysis model (R2 = 0.16, p = 0.01). Although performance in this test is strongly dependent on education level (Spearman r= 0.40, p b 0.00), in stepwise regression model analysis for 15-WLRT result as a depending variable this strong relationship did not reach statistical significance, on contrary to relationship with PBMC betaNGF, which remained statistically significant. The performance measures for this model are as follows: adjusted R2 = 0.42, p b 0.00; significant covariates include PBMC beta-NGF concentration (R= 0.4, p b 0.00) and age (R= −0.55, p b 0.00), nonsignificant covariates include years of education, BPF and total demyelinating plaque volume. Association between different neurotrophins' concentrations and cognitive performance in the study group are illustrated in Fig. 2. 3.4. Correlation between cognitive dysfunction, neuroradiological and clinical parameters Most of the neuropsychological tests' results correlated significantly with brain atrophy, total lesion volume, and EDSS score, as presented in Table 5. 4. Discussion In MS neurotrophins can activate several signaling pathways related to neuroprotection. They may exert their effects directly by acting on neuronal trk receptors, promoting their survival, or indirectly by interactions with trk receptors on different subpopulations of immune cells [26,27]. In this study we addressed the question whether neurotrophic status may be protective of cognitive decline in MS patients. We have found that PBMC-derived beta-NGF concentration is significantly positively correlated with spontaneous word list generation and 15-WLRT results in RRMS patients. The results of both these tests, assessing semantic and working memory, correlated strongly with the
Fig. 2. Association of NT-3 and beta-NGF PBMCs concentrations and cognitive performance in RRMS patients. Point size reflects the number of neuropsychological tests classified below the group median (cognitive composite score). PBMCs — peripheral blood mononuclear cells.
global cognitive deficit in the investigated group, as defined in our study. The relationship with word list generation test result, although identified by means of correlation, did not reach statistical significance in the stepwise regression model, being dominated by the strong impact of education and total lesion volume. On contrary, in stepwise regression model the impact of immune-cell derived beta-NGF on 15-WLRT result was shown to be stronger than the one of education level or radiological markers of MS-related damage. The fact that in our model the influence of education on 15-WLRT result is lost is obviously surprising. One possible explanation could be that in our study cohort education is strongly negatively correlated with age (Spearman r= −0.35, p = 0.02), namely young adults are better educated than the older ones. Given the strong association of 15-WLRT with age, it may be expected that the weaker associate, which in this case is education, would be deleted from the model. To summarize, we have shown for the first time that immune-cell beta-NGF expression may be protective of cognitive deficit in RRMS patients. This is in line with evidence obtained from animal models (see: Introduction) and with generally accepted fact that beta-NGF
Fig. 1. Scatterplots illustrating the relationship between PBMCs beta-NGF concentration and: spontaneous word list generation (WLG) test result (A) and 15-Word List Recall test (15-WLRT) result (B). Least square regression line shows the degree of association between the two variables. Correlation A is described by Pearson correlation coefficient r = 0.37 (p = 0.02). Correlation B is described by Pearson correlation coefficient r = 0.40 (p = 0.00).
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Table 5 Correlation matrices for neuropsychological tests results, neuroradiological and clinical parameters in the investigated group. Pearson or Spearman* correlation coefficient r (p)
Spontaneous word list generation tests 15-Word List Recall Test TMT-A [ms] TMT-B [ms] Visual digit span test Auditory digit span test Attention to detail Raven's Coloured Progressive Matrices (total score) RT Q1 RT median RT IQR RT Q3
BPF
Total lesion volume (cm3)
Corpus callosum cross-sectional area (mm2)
EDSS
r = 0.40 p b 0.00 r = 0.21 p = 0.18 r = −0.61 p b 0.00 *r = −0.62 p b 0.00 r = 0.58 p b 0.00 r = 0.56 p = 0.001 *r = 0.65 p b 0.00 r = 0.59 p b 0.00
*r= −0.49 p b 0.00 *r = −0.25 p = 0.11 *r= 0.43 p b 0.00 *r= 0.58 p b 0.00 *r= −0.46 p = 0.003 *r= −0.45 p = 0.003 *r= −0.48 p = 0.001 *r= −0.47 p = 0.002
r = 0.45 p b 0.00 r = 0.07 p = 0.66 r = −0.59 p b 0.00 *r = −0.59 p b 0.00 r = 0.50 p b 0.00 *r = 0.53 p b 0.000 *r = 0.45 p = 0.003 r = 0.49 p = 0.001
*r = −0.51 p b 0.00 *r = −0.52 p b 0.00 *r = 0.54 pb 0.00 *r = 0.65 pb 0.00 *r = −0.42 p b 0.00 *r = −0.29 p = 0.07 *r = −0.32 p = 0.04 *r = −0.51 p b 0.00
r = −0.23 r = −0.29 r = −0.08 r = −0.29
p = 0.16 p = 0.08 p = 0.63 p = 0.07
*r = 0.15 *r = 0.16 *r = 0.11 *r = 0.20
p = 0.37 p = 0.35 p = 0.50 p = 0.22
r = −0.11 p = 0.52 r = −0.13 p = 0.45 r = 0.02 p = 0.91 r = −0.14 p = 0.4
*r = 0.14 *r = 0.15 *r = 0.25 *r = 0.18
p = 0.41 p = 0.36 p = 0.13 p = 0.26
For normally or non-normally distributed variables Pearson or Spearman rank (marked with * sign) correlation coefficients were calculated, respectively. Statistically significant values are marked with bold type. IQR — interquartile range, Q1 — first quartile, Q3 — third quartile, TMT — trail making test, RT — reaction time.
is one of the factors facilitating survival and proper function of cholinergic neurons in the adult brain. It may be speculated that in MS, given a high level of neuronal destruction, there is also a higher demand for beta-NGF. In 2005 D'Intino et al. [11] showed that in Lewis rats with EAE memory and learning deficits correlated with lower activity of acetylcholinesterase and with lower beta-NGF mRNA expression in cortex, hippocampus and basal forebrain. Moreover, the use of selective acetylcholinesterase inhibitors (AChEI; rivastigmine and donepezil) improved cognitive functions and restored the proper acetylcholinesterase activity and beta-NGF expression. In this context our findings indirectly support the concept of treating MS-related cognitive deficit with AChEI. Although promising results were provided in a single-center randomized trial on donepezil use for this indication [28], larger multicenter trial failed to confirm its efficacy on memory impairment in MS patients [29]. Undoubtedly, future studies with other AChEI are needed. It is worth mentioning that NGF-mediated acetylocholine release is dependent upon extracellular choline availability [30]. Interestingly, choline concentration measured by 1H MR spectroscopy is lower in MS patients with no demeylination found in the routine MRI studies [31], which might mean it is being efficiently used for the intracellular acetylocholine synthesis. Such patients could constitute a distinct group with a higher neuroprotective potential. Different choline availability in different MS patients may influence the neuroprotective effect of beta-NGF and thus the correlation between cognitive variables and beta-NGF expression might not be straightforward. The next step in our research would be to combine immune cell-derived beta-NGF level measurements with neuropsychological and spectroscopy data in the same group of RRMS patients. Early identification of cognitive deficit in MS patients is crucial in the context of their life quality, giving possibility for an early introduction of psychotherapy and potential pharmacotherapy. Broad neuropsychological examination is time-consuming and cannot be recommended as a standard procedure in assessing MS patients. The widely used Paced Auditory Serial Addition Test is time-consuming, complicated and demanding for both, patients and doctors, and the learning effect has been shown [32]. We suggest that the simple, easily accessible tests — that in our study correlated with global cognitive deficit — might be used as screening markers for a generalized cognitive dysfunction in MS, namely: visual digit span, spontaneous word list generation tests, 15-WLRT, and attention to detail test. All these tests depend on the function of attention. Obviously, implementation of these tests into everyday practice requires validation. However, for now they can serve as useful tools in assessing the subtle interrelationships between cognitive, neuroradiological and neuroimmunological parameters in MS patients.
A possible limitation of our current study may be associated with the fact that cognitive functions decline slowly over time, while beta-NGF production by immune cells varies significantly during disease course, thus their interrelationship is expected to be non-linear and complex. Follow-up studies for both, neuroimmunological and neuropsychological parameters, are planned for the investigated group. It would also be interesting to investigate if any other neuroimmunological markers, such as inflammatory cytokines and chemokines, correlate with the level of cognitive dysfunction in MS patients. It seems noteworthy that while in our previous study [14] we showed that NT-3 and no other neurotrophin correlated with brain atrophy measures, in the same group of patients we have now identified a positive correlation between beta-NGF and semantic memory, which has not been observed for NT-3 or other neurotrophins. One of the possible explanations could be that different neurotrophins exert different biological roles, acting primarily on different populations of neurons. While beta-NGF has been linked to development and maintenance of the cholinergic phenotype of the basal forebrain and hippocampal neurons, such correlation was not noted for other neurotrophins, except for BDNF, which we did not analyze in this study. Also, NGF has been shown to prevent the loss of synaptic connections, thus facilitating synaptic plasticity [33]. Influence on the cholinergic innervation and modulation of activity dependent synaptic plasticity could account for the fact that it is NGF, and not other neurotrophins, that is linked to cognitive performance. On the other hand, NT-3 was shown to increase oligodendrocyte proliferation and differentiation of oligodendrocyte precursor cells [34], which facilitates neuroregeneration. Neuroregeneration can prevent brain atrophy accumulation. This may account for NT-3 correlation with brain atrophy measures. NT-3 seems more potent as a mediator of neuroregeneration, while NGF rather mediates neuroprotection, especially of the cholinergic phenotype. Also, in contrast to NGF, NT-3 is expressed mainly in the developing and not mature brain [35]. Therefore, even small reduction of adult baseline NT-3 levels can lead to significant functional disturbances that could be revealed also in a small cohort of patients. It might be that our sample size was too small to demonstrate all correlations, while revealing more prevalent ones. Another observation that seems noteworthy is that we detected NT-4/5 in nearly 80% of sera, but only in 25% of PBMCs samples, whereas for the other two neurotrophins our detection rate was the opposite. Based on the available literature, NT-4/5 seems to be the least potent of the neurotrophins we investigated. It can be hypothesized that its higher serum level is due to lower uptake within the damaged CNS, where more potent neurotrophins are used preferably. Another speculation could be that the peripheral source of NT-4/5 is different than PBMCs. NT-4/5 is ubiquitously expressed within the immune system, so different cell lines, including granulocytes and
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bone marrow cells, and not PBMCs, can provide additional secretion of NT-4/5. Our findings are preliminary; however, they do provide rationale for targeting NGF-pathway as a novel therapeutic strategy in MS. So far application of recombinant NGF has not been successful due to short half-life in vivo, difficulties crossing the blood–brain barrier, proteolytic degradation and potential side-effects [36]. NGF gene therapy is another option, already being tested for Alzheimer disease [37]. Small-molecule agonists for trk receptors would be yet another alternative. Interestingly, it has been shown that interferon beta, which is one of the currently approved and widely used immunomodulating agents for MS treatment, promotes NGF secretion early in the course of MS [38]. Such neuroprotective effect could at least partially contribute to interferon efficacy in MS. Immunomodulating and immunosuppressive therapies are only partially effective in MS. It may be that one should focus on implementing regenerating strategies instead. These would facilitate early and effective remyelination, thus preventing axonal degeneration. Targeting NGF-pathway may be an attractive option, especially in the context of MS-related cognitive deficit. Acknowledgements and conflict of interest All authors have nothing to disclose. M.A. Pawlak is funded by National Science Centre grant 2011/01/D/NZ4/05801. References [1] Lassman H. What drives disease in multiple sclerosis: inflammation or neurodegeneration? Clin Exp Neuroimmunol 2010;1:2–11. [2] Rao SM, Leo GJ, Bernardin L, Unverzagt F. Cognitive dysfunction in multiple sclerosis. Frequency patterns, and prediction. Neurology 1991;41:685-91. [3] Ron MA, Callanan MM, Warrington EK. Cognitive abnormalities in multiple sclerosis: a psychometric and MRI study. Psychol Med 1991;21:59-68. [4] Kujala P, Portin R, Ruutiainem J. Memory deficits and early cognitive deterioration in MS. Acta Neurol Scand 1996;93:329-35. [5] Piras MR, Magnano I, Canu EDG, Paulus KS, Satta WM, Soddu A, et al. Longitudinal study of cognitive dysfunction in multiple sclerosis: neuropsychological, neuroradiological and neurophysiological findings. J Neurol Neurosurg Psychiatry 2003;74:878-85. [6] Lewin GR, Barde Y-A. Physiology of the neurotrophins. Annu Rev Neurosci 1996;19:289-317. [7] Connor B, Dragunow M. The role of neuronal growth factors in neurodegenerative disorders of the human brain. Brain Res Rev 2009;27:1–39. [8] Chen KS, Nishimura MC, Armanini MP, Crowley C, Spencer SD, Phillips HS. Disruption of a single allele of the nerve growth factor gene results in atrophy of basal forebrain cholinergic neurons and memory deficits. J Neurosci 1997;17:7288-96. [9] Korte M, Carroll P, Wolf E, Brem G, Thoenen H, Bonhoeffer T. Hippocampal long-term potentiation is impaired in mice lacking brain-derived neurotrophic factor. Proc Natl Acad Sci U S A 1995;92:8856-60. [10] Mo L, Yang Z, Zhang A, Li X. The repair of the injured adult rat hippocampus with NT-3-chitosan carriers. Biomaterials 2010;31:2184-92. [11] D'Intino G, Paradisi M, Fernandez M, Giuliani A, Aloe L, Giardino L, et al. Cognitive deficit associated with cholinergic and nerve growth factor down-regulation in experimental allergic encephalomyelitis in rats. PNAS 2005;102:3070-5. [12] Patanella AK, Zinno M, Quaranta D, Nociti V, Frisullo G, Gainotti G, et al. Correlations between peripheral blood mononuclear cell production of BDNF, TNF-alpha, IL-6, IL-10 and cognitive performances in multiple sclerosis patients. J Neurosci Res 2010;88:1106-12. [13] Weinstock-Guttman B, Benedict RH, Tamaño-Blanco M, Ramasamy DP, Stosic M, Polito J, et al. The rs2030324 SNP of brain-derived neurotrophic factor (BDNF) is associated with visual cognitive processing in multiple sclerosis. Pathophysiology 2011;18:43-52.
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