Assessing the response to anti-IL-12 and -23 treatment for Alzheimer’s Disease using MRI

[This is a work in progress that will be updated shortly]

AD is a highly diffuse neurodegenerative pathology defined as a slow and inexorable progressive loss of cognitive functions, leading to dementia and death. The major histological features of Alzheimer’s disease (AD) are the presence of neurofibrillary tangles (NFTs), amyloid plaques and neuronal loss. AD brains are further characterised by cerebral atrophy and inflammation. Approximately 5% of AD patients have familial Alzheimer’s disease (FAD), a genetic predisposition in which mutations in several genes have been identified: amyloid precursor protein (APP); apolipoprotein (ApoE4); presenilin 1 (PS-1) and; presenilin 2 (PS-2). These genetic mutations have been used to create transgenic mouse models of AD that can reproduce several aspects of the disease. One critical aspect is the accumulation of amyloid plaques; protein deposits of aggregated amyloidogenic peptides, formed by the proteolysis of the membrane bound APP. The amyloid cascade hypothesis proposes that it is the accumulation of soluble and insoluble forms of Aβ that causes the onset of secondary alterations such as NFTs and neuronal death3.

 

Early diagnosis of AD is essential if therapies are to be implemented before widespread deterioration of mental function has occurred in these patients. The failures in clinical trials involving anti-Ab immunotherapy (most recent being bapineuzumab and solanezumab) support the widely held view that therapies targeting Ab must begin early in the course of disease. At the moment, diagnosis relies on clinical information, including performance on brief cognitive instruments of limited sensitivity, such as the Mini Mental State Examination (MMSE), with supportive findings from biochemical or imaging studies emerging only in the moderate to severe stages of the disease. In particular, it is essential to diagnose and treat those patients with mild cognitive impairment (MCI), who are most at risk of developing AD. Progress in this regard relies on further development of imaging-derived measures which will better elucidate the evolution of AD pathology. The establishment of new and reliable biomarkers of early AD will enable better understanding of the pathopsysiology, improved drug evaluation and eventually pave new methods for practical applications in clinical practice. To this end, we aim to develop MRI methods capable of measuring the extent of neurodegeneration and response to treatment by monitoring the changes in a preclinical model of AD using MRI. This prospect is of relevance given the common use of MRI as a surrogate tool.

 

Beta-amyloid plaques are the first lesions to develop in AD patients and are a potential disease marker for detection and target for treatment in the early stages of the disease. There is growing evidence that by targeting certain immunological components, beta-amyloid plaques and cognitive deficits can be improved. Aberrant immune reactions may strongly contribute to the pathogenesis of AD. The neuroinflammatory component of the disease points to an involvement of inflammatory genes as risk factors for AD (Jones et al., 2010; Guerreiro et al., 2012; Jonsson et al., 2012). Microglial cells respond to beta-amyloid load to increase the inflammatory load in the degenerating brain (Lucin and Wyss Coray, 2009). One key feature is the release of proinflammatory cytokines which may further drive inflammation, beta-amyloid deposition and contribute to neuronal death. Inflammation is usually a self-limiting process but the presence of Ab may act as a constant stimulus which helps to maintain a chronic inflammatory state.

 

A potential therapy will have to target specific inflammatory mechanisms at the earliest time points. Vom Berg et al., (2012)4 provided evidence for the disease promoting role of interleukins 12, and -23. Anti-IL-12 and -23 treatment decreased Ab load in APP/PS1 transgenic mice and ameliorated behavioural deficits. Findings of increased interleukins in human CSF from AD patients suggest that this may be a plausible target in AD4. Studies will have to identify the time course of this immunological pathway in AD and investigate ways to monitor the response to treatments.

 

The MRI detection of b-amyloid is an important line of investigation which will help the early diagnosis of Alzheimer’s disease. Identifying the substrates for the MR changes associated with Alzheimer’s disease pathology is crucial for evaluating treatment strategies. Alzheimer’s disease is multi-factorial and all constituents of AD lesions contribute to the MRI contrast in different ways. Direct visualisation of b-amyloid plaques is possible by using high field magnets5 but this method requires long imaging times and the high threshold of plaque size (~50 um) for detection which underestimates overall b-amyloid load. However, there are major challenges to performing such high resolution MRI for routine clinical studies. Furthermore, conventional relaxation-weighted imaging like T2-w imaging can be sensitive to pathological changes but are unspecific markers of pathology or response to therapy, and more specific quantitative MRI methods are needed. Recently, there have been reports of reduced quantitative T2 values in Alzheimer’s disease mouse models expressing b-amyloid6–8. With the complicated structure of brain tissue in mind, we took an alternative approach in our image analysis to perform correlation of relaxation time profiles with histology as a function of cortical depth2. By doing this we were able to demonstrate that the sensitivity towards MR changes can be increased and that there was a spatial pattern of T1 reductions which corresponded to the b-amyloid deposition in the 5xFAD mouse. We proposed that the reductions in T1 are due to the presence of Ab or its effects on the structure of brain tissue and as such it may be worth exploring the feasibility of using quantitative profile measures of T1 as a biomarker of Alzheimer’s disease pathology and treatment response. Quantitative MRI can be more specific for different tissue components and higher diagnostic value may be achieved by combining different MRI results9,10. We will also investigate the use of MTR and DTI to improve the characterisation of tissues by defining the correlations between MRI signal changes and histopathological findings.

 

MR relaxation reflects pathological processes related to parenchyma changes in water content such as glial proliferation, microglial activation, axonal and myelin loss and so is influenced by several types of pathology11. DTI quantifies extent of diffusivity and spatial restriction of water protons in certain directions (Basse et al., 1994) and can be used to track changes in white matter associated with neurodegeneration. A reduction of water diffusivity parallel to white matter fibre tracts is seen after axonal damage and an increase in diffusivity perpendicular to tracts is observed in demyelination9,10. In AD, FA reflects extent of astrogliosis, myelin loss and axonal loss. White matter injury caused by beta-amyloid deposition in AD mouse models have been detected using DTI12,13. Diffusion kurtosis imaging (DKI) is a diffusion-weighted MRI method which takes into account the non-Guasianity of diffusion of water molecules in brain tissue and so better represents the diffusion characteristics in this tissue environment. Using DKI, MRI signal changes due to amyloid deposition can be observed in brain regions, such as in grey matter regions, which are not seen using conventional DTI14,15.

 

Magnetisation transfer ratio (MTR) quantifies water protons closely associated with macromolecules. MTR is sensitive to the change in macromolecular structures on which water can be strongly associated with for example, myelin. An increase in MTR was found in an AD mouse model compared to non-AD controls and the MTR was reduced in the AD mouse with antioxidant treatment model16. Changes in MTR have also been found in human AD patients17,18.

 

References

1.        Spencer, N. G., Eykyn, T. R., deSouza, N. M. & Payne, G. S. The effect of experimental conditions on the detection of spermine in cell extracts and tissues. NMR in biomedicine 23, 163–9 (2010).

2.        Spencer, N. G. et al. Quantitative evaluation of MRI and histological characteristics of the 5xFAD Alzheimer mouse brain. NeuroImage 76, 108–115 (2013).

3.        Hardy, J. & Selkoe, D. J. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science (New York, N.Y.) 297, 353–6 (2002).

4.        Vom Berg, J. et al. Inhibition of IL-12/IL-23 signaling reduces Alzheimer’s disease-like pathology and cognitive decline. Nature medicine 18, 1812–9 (2012).

5.        Nakada, T., Matsuzawa, H., Igarashi, H., Fujii, Y. & Kwee, I. L. In vivo visualization of senile-plaque-like pathology in Alzheimer’s disease patients by MR microscopy on a 7T system. Journal of neuroimaging : official journal of the American Society of Neuroimaging 18, 125–9 (2008).

6.        El Tayara, N. E. T., Volk, A., Dhenain, M. & Delatour, B. Transverse relaxation time reflects brain amyloidosis in young APP/PS1 transgenic mice. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 58, 179–84 (2007).

7.        Helpern, J. a et al. MRI assessment of neuropathology in a transgenic mouse model of Alzheimer’s disease. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 51, 794–8 (2004).

8.        Braakman, N. et al. Longitudinal assessment of Alzheimer’s beta-amyloid plaque development in transgenic mice monitored by in vivo magnetic resonance microimaging. Journal of magnetic resonance imaging : JMRI 24, 530–6 (2006).

9.        Boretius, S. et al. Assessment of lesion pathology in a new animal model of MS by multiparametric MRI and DTI. NeuroImage 59, 2678–88 (2012).

10.      Janve, V. a. et al. The radial diffusivity and magnetization transfer pool size ratio are sensitive markers for demyelination in a rat model of type III multiple sclerosis (MS) lesions. NeuroImage 74, 298–305 (2013).

11.      Gouw, a a et al. Heterogeneity of white matter hyperintensities in Alzheimer’s disease: post-mortem quantitative MRI and neuropathology. Brain : a journal of neurology 131, 3286–98 (2008).

12.      Song, S.-K., Kim, J. H., Lin, S.-J., Brendza, R. P. & Holtzman, D. M. Diffusion tensor imaging detects age-dependent white matter changes in a transgenic mouse model with amyloid deposition. Neurobiology of disease 15, 640–7 (2004).

13.      Sun, S.-W. et al. Detection of age-dependent brain injury in a mouse model of brain amyloidosis associated with Alzheimer’s disease using magnetic resonance diffusion tensor imaging. Experimental neurology 191, 77–85 (2005).

14.      Vanhoutte, G. et al. Diffusion kurtosis imaging to detect amyloidosis in an APP/PS1 mouse model for Alzheimer’s disease. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 69, 1115–21 (2013).

15.      Falangola, M. F. et al. Age-related non-Gaussian diffusion patterns in the prefrontal brain. Journal of magnetic resonance imaging : JMRI 28, 1345–50 (2008).

16.      Pérez-Torres, C. J., Reynolds, J. O. & Pautler, R. G. Use of Magnetization Transfer Contrast MRI to Detect Early Molecular Pathology in Alzheimer’s Disease. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine (2013).doi:10.1002/mrm.24665

17.      Kabani, N. J., Sled, J. G. & Chertkow, H. Magnetization transfer ratio in mild cognitive impairment and dementia of Alzheimer’s type. NeuroImage 15, 604–10 (2002).

18.      Ridha, B. H. et al. Magnetization transfer ratio in Alzheimer disease: comparison with volumetric measurements. AJNR. American journal of neuroradiology 28, 965–70 (2007).

19.      Dong, Y.-F. et al. Perindopril, a centrally active angiotensin-converting enzyme inhibitor, prevents cognitive impairment in mouse models of Alzheimer’s disease. FASEB journal : official publication of the Federation of American Societies for Experimental Biology 25, 2911–20 (2011).