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Posts Tagged ‘Alzheimers Disease’


Disentangling molecular alterations from water-content changes in the aging human brain using quantitative MRI

Reporter: Dror Nir, PhD

 

Authors’ list: Shir Filo, Oshrat Shtangel, Noga Salamon, Adi Kol, Batsheva Weisinger, Sagiv Shifman & Aviv A. Mezer
Published in: Nature Communications volume 10, Article number: 3403 (2019)

Abstract

It is an open question whether aging-related changes throughout the brain are driven by a common factor or result from several distinct molecular mechanisms. Quantitative magnetic resonance imaging (qMRI) provides biophysical parametric measurements allowing for non-invasive mapping of the aging human brain. However, qMRI measurements change in response to both molecular composition and water content. Here, we present a tissue relaxivity approach that disentangles these two tissue components and decodes molecular information from the MRI signal. Our approach enables us to reveal the molecular composition of lipid samples and predict lipidomics measurements of the brain. It produces unique molecular signatures across the brain, which are correlated with specific gene-expression profiles. We uncover region-specific molecular changes associated with brain aging. These changes are independent from other MRI aging markers. Our approach opens the door to a quantitative characterization of the biological sources for aging, that until now was possible only post-mortem.

 

Introduction

The biology of the aging process is complex, and involves various physiological changes throughout cells and tissues1. One of the major changes is atrophy, which can be monitored by measuring macroscale brain volume reduction1,2. In some cases, atrophy can also be detected as localized microscale tissue loss reflected by increased water content3. This process is selective for specific brain regions and is thought to be correlated with cognitive decline in Alzheimer’s disease2,4,5. In addition to atrophy, there are molecular changes associated with the aging of both the normal and pathological brain5,6. Specifically, lipidome changes are observed with age, and are associated with several neurological diseases7,8,9,10,11.

It is an open question as to whether there are general principles that govern the aging process, or whether each system, tissue, or cell deteriorates with age for different reasons12,13. On one hand, the common-cause hypothesis proposes that different biological aging-related changes are the result of a single underlying factor14,15. This implies that various biomarkers of aging will be highly correlated16. On the other hand, the mosaic theory of aging suggests that there are several distinct aging mechanisms that have a heterogenous effect throughout the brain12,13. According to this latter view, combining different measurements of brain tissue is crucial in order to fully describe the state of the aging brain. To test these two competing hypotheses in the context of volumetric and molecular aging-related changes, it is essential to measure different biological aspects of brain tissue. Unfortunately, the molecular correlates of aging are not readily accessible by current in vivo imaging methods.

The main technique used for non-invasive mapping of the aging process in the human brain is magnetic resonance imaging (MRI)2,17,18,19. Advances in the field have led to the development of quantitative MRI (qMRI). This technique provides biophysical parametric measurements that are useful in the investigation and diagnosis of normal and abnormal aging20,21,22,23,24,25,26,27. qMRI parameters have been shown to be sensitive to the microenvironment of brain tissue and are therefore named in vivo histology28,29,30. Nevertheless, an important challenge in applying qMRI measurements is increasing their biological interpretability. It is common to assume that qMRI parameters are sensitive to the myelin fraction20,23,30,31,32,33, yet any brain tissue including myelin is a mixture of multiple lipids and proteins. Moreover, since water protons serve as the source of the MRI signal, the sensitivity of qMRI parameters to different molecular microenvironments may be confounded by their sensitivity to the water content of the tissue34,35. We hypothesized that the changes observed with aging in MRI measurements20,23,30,31,32,33,36 such as R1, R2, mean diffusivity (MD), and magnetization transfer saturation (MTsat)37, could be due to a combination of an increase in water content at the expense of tissue loss, and molecular alterations in the tissue.

Here, we present a qMRI analysis that separately addresses the contribution of changes in molecular composition and water content to brain aging. Disentangling these two factors goes beyond the widely accepted “myelin hypothesis” by increasing the biological specificity of qMRI measurements to the molecular composition of the brain. For this purpose, we generalize the concept of relaxivity, which is defined as the dependency of MR relaxation parameters on the concentration of a contrast agent38. Instead of a contrast agent, our approach exploits the qMRI measurement of the local non-water fraction39 to assess the relaxivity of the brain tissue itself. This approach allows us to decode the molecular composition from the MRI signal. In samples of known composition, our approach provides unique signatures for different brain lipids. In the live human brain, it produces unique molecular signatures for different brain regions. Moreover, these MRI signatures agree with post-mortem measurements of the brain lipid and macromolecular composition, as well as with specific gene-expression profiles. To further validate the sensitivity of the relaxivity signatures to molecular composition, we perform direct comparison of MRI and lipidomics on post-mortem brains. We exploit our approach for multidimensional characterization of aging-related changes that are associated with alterations in the molecular composition of the brain. Finally, we evaluate the spatial pattern of these changes throughout the brain, in order to compare the common-cause and the mosaic theories of aging in vivo.

 

Results

Different brain lipids have unique relaxivity signatures
The aging process in the brain is accompanied by changes in the chemophysical composition, as well as by regional alterations in water content. In order to examine the separate pattern of these changes, we developed a model system. This system was based on lipid samples comprising common brain lipids (phosphatidylcholine, sphingomyelin, phosphatidylserine, phosphatidylcholine-cholesterol, and phosphatidylinositol-phosphatidylcholine)7. Using the model system, we tested whether accounting for the effect of the water content on qMRI parameters provides sensitivity to fine molecular details such as the head groups that distinguish different membrane phospholipids. The non-water fraction of the lipid samples can be estimated by the qMRI measurement of lipid and macromolecular tissue volume (MTV, for full glossary of terms see Supplementary Table 1)39. By varying the concentration of the lipid samples, we could alter their MTV and then examine the effect of this manipulation on qMRI parameters. The parameters we estimated for the lipid samples were R1, R2, and MTsat. The potential ambiguity in the biological interpretation of qMRI parameters is demonstrated in Fig. 1a. On one hand, samples with similar lipid composition can present different R1 measurements (Fig. 1a, points 1 & 2). On the other hand, scanning samples with different lipid compositions may result in similar R1 measurements (Fig. 1a, points 2 & 3). This ambiguity stems from the confounding effect of the water content on the MR relaxation properties.

Screenshot 2019-08-01 at 14.36.20

We evaluated the dependency of different qMRI parameters on the non-water fraction estimated by MTV. This analysis revealed strong linear dependencies (median R2 = 0.74, Fig. 1a, b and Supplementary Fig. 1a, b). These linear MTV dependencies change as a function of the lipid composition, reflecting the inherent relaxivity of the different lipids. We could therefore use the MTV derivatives of qMRI parameters (dqMRIdMTV, i.e., the slope of the linear relationship between each qMRI parameter and MTV) as a measure that is sensitive to molecular composition. By accounting for the Multidimensional Dependency on MTV (“MDM”) of several qMRI parameters, a unique MRI relaxivity signature was revealed for each lipid (Fig. 1c). This implies that the water-related ambiguity demonstrated in the inset of Fig. 1a can be removed by measuring the MTV dependencies (Fig. 1c). Creating mixtures of several lipids provided supportive evidence for the generality of our framework. Figure 1d and Supplementary Fig. 1c show that the qMRI measurements of a mixture can be predicted by summing the MTV dependencies of pure lipids (for further details see Supplementary Note 1 and Supplementary Fig. 2). Furthermore, we used this biophysical model to predict the lipid composition of a mixture from its MDM measurements (Fig. 1e). This model provided a good estimation of the sphingomyelin (Spg) and phosphatidylserine (PS) content (R2 > 0.64) but failed to predict phosphatidylcholine (PtdCho) content (for further details see Supplementary Note 2). While lipids are considered to be a major source of the MRI signal in the brain 40,41,42,43,44,45, our approach can be applied to other compounds to reveal differences in the MRI signal between different proteins, sugars, and ions (Supplementary Fig. 1d). Hence, the relationships between qMRI parameters and MTV account for the effect of water on MRI measurements and could be of use in quantifying the biological and molecular contributions to the MRI signal of water protons.

The tissue relaxivity of the human brain is region-specific.
In order to target age-related changes in molecular composition, we applied the same approach for the human brain (Fig. 2a).

Screenshot 2019-08-01 at 14.41.35

We found that the linear dependency of qMRI parameters on MTV is not limited to in vitro samples and a similar relationship was also evident in the human brain (Fig. 2b and Supplementary Figs. 3–5). Importantly, different brain regions displayed a distinct dependency on MTV. Therefore, the relaxivity of brain tissue is region-specific. Figure 2b provides an example for the regional linear trends of R1 and MTsat in a single subject. Remarkably, while the thalamus and the pallidum presented relatively similar R1 dependencies on MTV, their MTsat dependencies were different (p < 0.001, two-sample t-test). Compared to these two brain regions, frontal white-matter demonstrated different dependencies on MTV (p < 0.001, two-sample t-test). A better separation between brain regions can therefore be achieved by combining the MTV dependencies of several qMRI parameters (MTsat, MD, R1 and R2). The MTV derivatives of qMRI parameters are consistent across subjects (Fig. 2c and Supplementary Fig. 6), with good agreement between hemispheres (Supplementary Fig. 5). Moreover, they provide a novel pattern of differentiation between brain regions, which is not captured by conventional qMRI methods (Supplementary Fig. 7). In our lipid sample experiments, the MDM approach revealed unique relaxivity signatures of different lipids (Fig. 1c). Therefore, we attribute the observed diversity in the MTV derivatives of qMRI parameters across brain regions to the intrinsic heterogeneity in the chemophysical microenvironment of these regions. The multidimensional dependency of various qMRI parameters on MTV can be represented by the space of MTV derivatives to reveal a unique chemophysical MDM signature for different brain regions (Fig. 2d, see explanatory scheme of the MDM method in Supplementary Fig. 8). Fig. 2 figure2 The MDM method provides region-specific signatures in the in vivo human brain. a Representative MTV, MTsat, and R1 maps. b Calculating the MDM signatures. The dependency of R1 (left) and MTsat (right) on MTV in three brain regions of a single subject. For each region, MTV values were pooled into bins (dots are the median of each bin; shaded area is the median absolute deviation), and a linear fit was calculated (colored lines). The slopes of the linear fit represent the MTV derivatives of R1 and MTsat and vary across brain regions. c The reliability of the MDM method across subjects. Variation in the MTV derivatives of R1 (left) and MTsat (right) in young subjects (N = 23). Different colors represent 14 brain regions (see legend). Edges of each box represent the 25th, and 75th percentiles, median is in black, and whiskers extends to extreme data points. Different brain regions show distinct MTV derivatives. d Unique MDM signatures for different brain regions (in different colors). Each axis is the MTV derivative (“MDM measurements”) of a different qMRI parameter (R1, MTsat, R2, and MD). The range of each axis is in the legend. Colored traces extend between the MDM measurements, shaded areas represent the variation across subjects (N = 23). An overlay of all MDM signatures is marked with dashed lines Full size image The in vivo MDM approach captures ex vivo molecular profiles To validate that the MDM signatures relate to the chemophysical composition of brain tissue, we compared them to a previous study that reported the phospholipid composition of the human brain7. First, we established the comparability between the in vivo MRI measurements and the reported post-mortem data. MTV measures the non-water fraction of the tissue, a quantity that is directly related to the total phospholipid content. Indeed, we found good agreement between the in vivo measurement of MTV and the total phospholipid content across brain regions (R2 = 0.95, Fig. 3a). Söderberg et al.7 identified a unique phospholipid composition for different brain regions along with diverse ratios of phospholipids to proteins and cholesterol. We compared this regional molecular variability to the regional variability in the MDM signatures. To capture the main axes of variation, we performed principal component analysis (PCA) on both the molecular composition of the different brain regions and on their MDM signatures. For each of these two analyses, the first principal component (PC) explained >45% of the variance. The regional projection on the first PC of ex vivo molecular composition was highly correlated (R2 = 0.84, Fig. 3b) with the regional projection on the first PC of in vivo MDM signatures. This confirms that brain regions with a similar molecular composition have similar MDM. Supplementary Fig. 9a provides the correlations of individual lipids with MDM. Importantly, neither MTV nor the first PC of standard qMRI parameters was as strongly correlated with the ex vivo molecular composition as the MDM (Supplementary Fig. 9b, c). We next used the MDM measurements as predictors for molecular properties of different brain regions. Following our content predictions for lipids samples (Fig. 1e), we constructed a weighted linear model for human data (for further details see Supplementary Note 3). To avoid over fitting, we reduced the number of fitted parameters by including only the MDM and the molecular features that accounted for most of the regional variability. The MTV derivatives of R1 and MTsat accounted for most of the variance in MDM. Thus, we used these parameters as inputs to the linear model, while adjusting their weights through cross validation. We tested the performance of this model in predicting the three molecular features that account for most of the variance in the ex vivo molecular composition. Remarkably, MRI-driven MDM measurements provided good predictions for the regional sphingomyelin composition (R2 = 0.56, p < 0.05 for the F-test, Fig. 3c) and the regional ratio of phospholipids to proteins (R2 = 0.56, p < 0.05 for the F-test, Fig. 3c).

Screenshot 2019-08-01 at 14.44.06
Last, we compared the cortical MDM signatures to a gene co-expression network based on a widespread survey of gene expression in the human brain46. Nineteen modules were derived from the gene network, each comprised of a group of genes that co-varies in space. Six out of the nineteen gene modules were significantly correlated with the first PC of MDM. Interestingly, the first PC of MDM across the cortex was correlated most strongly with the two gene modules associated with membranes and synapses (Fig. 4, for further details see Supplementary Note 4 and Supplementary Figs. 10 and 11).

Screenshot 2019-08-01 at 14.47.04

Post-mortem validation for the lipidomic sensitivity of MDM.
The aforementioned analyses demonstrate strong agreement between in vivo MDM measurements and ex vivo molecular composition based on a group-level comparison of two different datasets. Strikingly, we were able to replicate this result at the level of the single brain. To achieve this we performed MRI scans (R1, MTsat, R2, MD, and MTV mapping) followed by histology of two fresh post-mortem porcine brains (Fig. 5a, b). First, we validated the qMRI estimation of MTV using dehydration techniques. MTV values estimated using MRI were in agreement with the non-water fraction found histologically (adjusted R2 = 0.64, p < 0.001 for the F-test, Fig. 5c).

Screenshot 2019-08-01 at 14.50.12
Next, we estimated the lipid composition of different brain regions. Thin-layer chromatography (TLC) was employed to quantify seven neutral and polar lipids (Supplementary Table 2 and Supplementary Fig. 12a). In accordance with the analysis in Fig. 3, we performed PCA to capture the main axes of variation in lipidomics, standard qMRI parameters, and MDM. Figure 5d shows that MTV did not correlate with the molecular variability across the brain, estimated by the 1st PC of lipidomics. Likewise, the molecular variability did not agree with the 1st PC of standard qMRI parameters (Fig. 5e).

Last, we applied the MDM approach to the post-mortem porcine brain. Similar to the human brain, different porcine brain regions have unique MDM signatures (Fig. 5f, g and Supplementary Fig. 12b). Remarkably, we found that agreement between lipid composition and MRI measurements emerges at the level of the MDM signatures. The molecular variability across brain regions significantly correlated with the regional variability in the MDM signatures (adjusted R2 = 0.3, p < 0.01 for the F-test, Fig. 5h). Excluding from the linear regression five outlier brain regions where the histological lipidomics results were 1.5 standard deviations away from the center yielded an even stronger correlation between MDM signatures and lipid composition (adjusted R2 = 0.55, p < 0.001 for the F-test, Supplementary Fig. 12c). This post-mortem analysis validates that the MDM approach allows us to capture molecular information using MRI at the level of the individual brain.

Disentangling water and molecular aging-related changes.
After establishing the sensitivity of the MDM signatures to the molecular composition of the brain, we used them to evaluate the chemophysical changes of the aging process. To assess aging-related changes across the brain, we scanned younger and older subjects (18 older adults aged 67 ± 6 years and 23 younger adults aged 27 ± 2 years). First, we identified significant molecular aging-related changes in the MDM signatures of different brain regions (Figs. 6 and 7, right column; Supplementary Fig. 13). Next, we tested whether the changes in MRI measurements, observed with aging, result from a combination of changes in the molecular composition of the tissue and its water content. We found that although it is common to attribute age-related changes in R1 and MTsat to myelin28,30,36, these qMRI parameters combine several physiological aging aspects. For example, using R1 and MTsat we identified significant aging-related changes in the parietal cortex, the thalamus, the parietal white-matter and the temporal white-matter (Figs. 6 and 7, left column). However, the MDM approach revealed that these changes have different biological sources (Figs. 6 and 7, middle columns; see Supplementary Figs. 14–17 for more brain regions).

Screenshot 2019-08-01 at 14.51.53

Screenshot 2019-08-01 at 14.54.44

Screenshot 2019-08-01 at 14.56.06

In agreement with the mosaic hypothesis, we identified distinct aging patterns for different brain regions. For example, in the hippocampus we found a change in R2* values related to a higher iron concentration with age, along with significant reduction in the total hippocampal volume (Fig. 8a). This age-related shrinkage was not accompanied by lower MTV values, indicating conserved tissue density (Fig. 7b). In addition, there was no significant difference in the hippocampal MDM signature with age (Fig. 7b). Cortical gray-matter areas also exhibited similar trends of volume reduction without major loss in tissue density (Fig. 8a). Unlike the gray matter, in the white matter we did not find volume reduction or large iron accumulation with age (Fig. 8a). However, we did find microscale changes with age in tissue composition, as captured by the MDM signature (Figs. 6a and 7c, and Supplementary Fig. 13), accompanied by a significant density-related decline in MTV (Fig. 8a). These findings are consistent with previous histological studies49,50,51 (see Discussion), and provide the ability to monitor in vivo the different components of the aging mosaic.

Last, to test whether the different biological aging trajectories presented in Fig. 8a share a common cause, we evaluated the correlations between them (Fig. 8b). Importantly, the chemophysical trajectory did not correlate significantly with the iron or volume aging patterns. The spatial distribution of water-related changes was found to correlate with iron content alterations (R2 = 0.27) and chemophysical alterations (R2 = 0.25). However, the strongest correlation between aging-related changes was found in volume and iron content (R2 = 0.77). As shown previously, this correlation may be explained to some extent by a systematic bias in automated tissue classification23. Additional analysis revealed that the different dimensions of the MDM signature capture distinct patterns of aging-related changes (Supplementary Fig. 30). Hence, complementary information regarding the various chemophysical mechanisms underlying brain aging could be gained by combining them.

 

Discussion

Normal brain aging involves multiple changes, at both the microscale and macroscale level. MRI is the main tool for in vivo evaluation of such age-related changes in the human brain. Here, we propose to improve the interpretation of MRI findings by accounting for the fundamental effect of the water content on the imaging parameters. This approach allows for non-invasive mapping of the molecular composition in the aging human brain.

Our work is part of a major paradigm shift in the field of MRI toward in vivo histology30,36,52. The MDM approach contributes to this important change by providing a hypothesis-driven biophysical framework that was rigorously developed. We demonstrated the power of our framework, starting from simple pure lipid phantoms to more complicated lipid mixtures, and from there, to the full complexity of the brain. In the brain, we show both in vivo and post-mortem validations for the molecular sensitivity of the MDM signatures. Early observations relate different qMRI parameters to changes in the fraction of myelin20,23,30,31,32,33,36. The current approach enriches this view and provides better sensitivity to the molecular composition and fraction of myelin and other cellular tissues.

We developed a unique phantom system of lipid samples to validate our method. While the phantom system is clearly far from the complexity of brain tissue, its simplicity allowed us to verify the specificity of our method to the chemophysical environment. Remarkably, our approach revealed unique signatures for different lipids, and is therefore sensitive even to relatively subtle details that distinguish one lipid from another. We chose to validate our approach using membrane lipids based on previous experiments40,41,42,43,44,45. Nevertheless, we do acknowledge the fact that brain tissue comprises many other compounds beside lipids, such as proteins, sugars, and ions. As we have shown, these other compounds also exhibit unique dependency on MTV. The effect of such compounds, along with other factors such as microstructure, and multi-compartment organization28 is probably captured when we apply the MDM approach to the in vivo human brain. Therefore, the phantoms were made to examine the MRI sensitivity for the chemophysical environment, and the human brain data was used to measure the true biological effects in a complex in vivo environment.

Our relaxivity approach captures the molecular signatures of the tissue, but is limited in its abilities to describe the full complexity of the chemophysical environment of the human brain. For example, R1 and R2, which are used to generate the MDM signatures, are also sensitive to the iron content23,48,52. However, we found that most of our findings cannot be attributed to alterations in iron content as measured with R2* (for more details see Supplementary Note 5). While there is great importance in further isolating different molecular components, we argue that accounting for the major effect of water on qMRI parameters (for R2 distributions see Supplementary Fig. 5) is a crucial step towards more specific qMRI interpretation.

We provide evidence from lipids samples and post-mortem data for the sensitivity of the MDM signatures to the molecular environment (Figs. 1e, 3b, and 5h). The variability of MDM values between human brain regions also correlated with specific gene-expression profiles (Fig. 4). While the comparison of in vivo human brain measurements to previously published ex vivo findings is based on two different datasets, these measurements are highly stable across normal subjects and the intersubject variabilities are much smaller than the regional variability. The agreement between the modalities provides strong evidence for the ability of our method to capture molecular information.

Remarkably, we were able to demonstrate the sensitivity of MDM signatures to lipid composition using direct comparison on post-mortem porcine brains. Even though there are many challenges in scanning post-mortem tissue, segmenting it, and comparing it to anatomically relevant histological results, we were able to replicate our in vivo findings. We provide histological validation for the MRI estimation of MTV. Moreover, we find that while standard qMRI parameters and MTV do not explain the lipidomic variability across the brain, the MDM signatures are in agreement with histological results. Lipids constitute the majority of the brain’s dry weight and are known to be important for maintaining neural conduction and chemical balance53,54. The brain lipidome was shown to have a great deal of structural and functional diversity and was found to vary according to age, gender, brain region, and cell type55. Disruptions of the brain lipid metabolism have been linked to different disorders, including Alzheimer’s disease, Parkinson’s disease, depression, and anxiety7,8,11,54,55,56,57. Our results indicate that the MDM approach enhances the consistency between MRI-driven measurements and lipidomics, compared with standard qMRI parameters.

The simplicity of our model, which is based on a first-order approximation of qMRI dependencies, has great advantages in the modeling of complex environments. Importantly, we used lipids samples to show that the contributions of different mixture-components can be summed linearly (Fig. 1d). For contrast agents, the relaxivity is used to characterize the efficiency of different agents. Here, we treated the tissue itself, rather than a contrast material, as an agent to compute the relaxivity of the tissue. While relaxivity is usually calculated for R1 and R2, we extended this concept to other qMRI parameters. Our results showed that the tissue relaxivity changes as a function of the molecular composition. This suggests that the relaxivity of the tissue relates to the surface interaction between the water and the chemophysical environment. A theoretical formulation for the effect of the surface interaction on proton relaxation has been proposed before58,59. Specifically, a biophysical model for the linear relationship between R1 and R2 to the inverse of the water content (1/WC = 1/(1 – MTV)) was suggested by Fullerton et al.43. Interestingly, 1/WC varies almost linearly with MTV in the physiological range of MTV values. Applying our approach with 1/WC instead of MTV produces relatively similar results (Supplementary Fig. 28). However, using MTV as a measure of tissue relaxivity allowed us to generalize the linear model to multiple qMRI parameters, thus producing multidimensional MDM signatures.

We show that the MDM signatures allow for better understanding of the biological sources for the aging-related changes observe with MRI. Normal brain aging involves multiple changes, at both the microscale and macroscale levels. Measurements of macroscale brain volume have been widely used to characterize aging-associated atrophy. Our method of analysis can complement such findings and provide a deeper understanding of microscale processes co-occurring with atrophy. Moreover, it allows us to test whether these various microscale and macroscale processes are caused by a common factor or represent the aging mosaic. Notably, we discovered that different brain regions undergo different biological aging processes. Therefore, combining several measurements of brain tissue is crucial in order to fully describe the state of the aged brain. For example, the macroscale aging-related volume reduction in cortical gray areas was accompanied by conserved tissue density, as estimated by MTV, and region-specific chemophysical changes, as estimated by the MDM. In contrast, in white-matter areas both MDM and MTV changed with age. These microscale alterations were not accompanied by macroscale volume reduction. Our in vivo results were validated by previous histological studies, which reported that the cortex shrinks with age, while the neural density remains relatively constant49,50. In contrast, white matter was found to undergo significant loss of myelinated nerve fibers during aging51. In addition, we found that the shrinkage of the hippocampus with age is accompanied with conserved tissue density and chemophysical composition. This is in agreement with histological findings, which predict drastic changes in hippocampal tissue composition in neurological diseases such as Alzheimer, but not in normal aging49,50,60,61. In contrast, hippocampal macroscale volume reduction was observed in both normal and pathological aging2.

It should be noted that most of the human subjects recruited for this study were from the academic community. However, the different age groups were not matched for variables such as IQ and socioeconomic status. In addition, the sample size in our study was quite small. Therefore, the comparison we made between the two age groups may be affected by variables other than age. Our approach may benefit from validation based on larger quantitative MRI datasets27,62. Yet, we believe we have demonstrated the potential of our method to reveal molecular alterations in the brain. Moreover, the agreement of our findings with previous histological aging studies supports the association between the group differences we measured and brain aging. Our results suggest that the MDM approach may be very useful in differentiating the effects of normal aging from those of neurodegenerative diseases. There is also great potential for applications in other brain research fields besides aging. For example, our approach may be used to advance the study and diagnosis of brain cancer, in which the lipidomic environment undergoes considerable changes63,64,65.

To conclude, we have presented here a quantitative MRI approach that decodes the molecular composition of the aging brain. While common MRI measurements are primarily affected by the water content of the tissue, our method employed the tissue relaxivity to expose the sensitivity of MRI to the molecular microenvironment. We presented evidence from lipid samples, post-mortem porcine brains and in vivo human brains for the sensitivity of the tissue relaxivity to molecular composition. Results obtained by this method in vivo disentangled different biological processes occurring in the human brain during aging. We identified region-specific patterns of microscale aging-related changes that are associated with the molecular composition of the human brain. Moreover, we showed that, in agreement with the mosaic theory of aging, different biological age-related processes measured in vivo have unique spatial patterns throughout the brain. The ability to identify and localize different age-derived processes in vivo may further advance human brain research.

Methods

Phantom construction
The full protocol of lipids phantom preparation is described in Shtangel et al.66.

In short, we prepared liposomes from one of the following lipids: phosphatidylserine (PS), phosphatidylcholine (PtdCho), phosphatidylcholine-cholesterol (PtdCho-Chol), Phosphatidylinositol-phosphatidylcholine (PI-PtdCho), or sphingomyelin (Spg). These phantoms were designed to model biological membranes and were prepared from lipids by the hydration–dehydration dry film technique67. The lipids were dissolved over a hot plate and vortexed. Next, the solvent was removed to create a dry film by vacuum-rotational evaporation. The samples were then stirred on a hot plate at 65 °C for 2.5 h to allow the lipids to achieve their final conformation as liposomes. Liposomes were diluted with Dulbecco’s phosphate buffered saline (PBS), without calcium and magnesium (Biological Industries), to maintain physiological conditions in terms of osmolarity, ion concentrations and pH. To change the MTV of the liposome samples we varied the PBS to lipid volume ratios66. Samples were then transferred to the phantom box for scanning in a 4 mL squared polystyrene cuvettes glued to a polystyrene box, which was then filled with ~1% SeaKem Agarose (Ornat Biochemical) and ~0.0005 M Gd (Gadotetrate Melumine, (Dotarem, Guerbet)) dissolved in double distilled water (ddw). The purpose of the agar with Gd (Agar-Gd) was to stabilize the cuvettes, and to create a smooth area in the space surrounding the cuvettes that minimalized air–cuvette interfaces. In some of our experiments we used lipid mixtures composed of several lipids. We prepared nine mixtures containing different combinations of two out of three lipids (PtdChol, Spg and PS) in varying volume ratios (1:1,1:2,2:1). For each mixture, we prepared samples in which the ratio between the different lipid components remained constant while the water-to-lipid volume fraction varied.

For the bovine serum albumin (BSA) phantoms, samples were prepared by dissolving lyophilized BSA powder (Sigma Aldrich) in PBS. To change the MTV of these phantoms, we changed the BSA concentration. For the BSA + Iron phantoms, BSA was additionally mixed with a fixed concentration of 50 µg/mL ferrous sulfate heptahydrate (FeSO4*7H2O). Samples were prepared in their designated concentrations at room temperature. Prepared samples were allowed to sit overnight at 4 ℃ to ensure BSA had fully dissolved, without the need for significant agitation, which is known to cause protein cross-linking. Samples were then transferred to the phantom box for scanning.

For Glucose and Sucrose phantoms, different concentrations of D-( + )-Sucrose (Bio-Lab) and D-( + )-Glucose (Sigma) were dissolved in PBS at 40 ℃. Samples were allowed to reach room temperature before the scan.

MRI acquisition for phantoms

Data was collected on a 3 T Siemens MAGNETOM Skyra scanner equipped with a 32-channel head receive-only coil at the ELSC neuroimaging unit at the Hebrew University.

For quantitative R1 & MTV mapping, three-dimensional (3D) Spoiled gradient (SPGR) echo images were acquired with different flip angles (α = 4°, 8°, 16°, and 30°). The TE/TR was 3.91/18 ms. The scan resolution was 1.1 × 1.1 × 0.9 mm. The same sequence was repeated with a higher resolution of 0.6 × 0.6 × 0.5 mm. The TE/TR was 4.45/18 ms. For calibration, we acquired an additional spin-echo inversion recovery (SEIR) scan. This scan was done on a single slice, with adiabatic inversion pulse and inversion times of TI = 2000, 1200, 800, 400, and 50. The TE/TR was 73/2540 ms. The scan resolution was 1.2 mm isotropic.

For quantitative T2 mapping, images were acquired with a multi spin-echo sequence with 15 equally spaced spin echoes between 10.5 ms and 157.5 ms. The TR was 4.94 s. The scan resolution was 1.2 mm isotropic. For quantitative MTsat mapping, images were acquired with the FLASH Siemens WIP 805 sequence. The TR was 23 ms for all samples except PI:PtdCho for which the TR was 72 ms. Six echoes were equally spaced between 1.93 ms to 14.58 ms. The on-resonance flip angle was 6°, the MT flip angle was 220°, and the RF offset was 700. We used 1.1-mm in-plane resolution with a slice thickness of 0.9 mm. For samples of sucrose and glucose, MTsat mapping was done similar to the human subjects, based on 3D Spoiled gradient (SPGR) echo image with an additional MT pulse. The flip angle was 10°, the TE/TR was 3.91/28 ms. The scan resolution was 1 mm isotropic.

Estimation of qMRI parameters for phantoms

MTV and R1 estimations for the lipids samples were computed based on a the mrQ39 (https://github.com/mezera/mrQ) and Vista Lab (https://github.com/vistalab/vistasoft/wiki) software. The mrQ software was modified to suit the phantom system66. The modification utilizes the fact that the Agar-Gd filling the box around the samples is homogeneous and can, therefore, be assumed to have a constant T1 value. We used this gold standard T1 value generated from the SEIR scan to correct for the excite bias in the spoiled gradient echo scans. While the data was acquired in two different resolutions (see “MRI acquisition”), in our analysis we use the median R1 and MTV of each lipid sample and these are invariant to the resolution of acquisition (Supplementary Fig. 1e). Thus, we were able to use scans with different resolutions without damaging our results. T2 maps were computed by implementing the echo‐modulation curve (EMC) algorithm68.

For quantitative MTsat mapping see the “MTsat estimation” section for human subjects.

MDM computation for phantoms

We computed the dependency of each qMRI parameter (R1, MTsat, and R2) on MTV in different lipids samples. This process was implemented in MATLAB (MathWorks, Natwick, MI, USA). To manipulate the MTV values, we scanned samples of the same lipid in varying concentrations. We computed the median MTV of each sample, along with the median of qMRI parameters. We used these data points to fit a linear model across all samples of the same lipid. The slope of this linear model represents the MTV derivative of the linear equation. We used this derivative estimate of three qMRI parameters (R1, R2, and MTsat) to compute the MDM signatures. The same procedure was used for the MDM computation of lipid mixtures.

 

MDM modeling of lipid mixtures

We tested the ability of MDM to predict the composition of lipid mixtures. For this analysis we used nine mixture phantoms (see “Phantom construction”), along with the three phantoms of the pure lipid constituents of the mixtures (PS, Spg, and Ptd-Cho).

In order to predict the qMRI parameters of a lipid mixture (Fig. 1d) we used Supplementary Eq. 1 (Supplementary Note 1). To further predict the composition of the mixtures (Fig. 1e) we used Supplementary Eq. 5 (Supplementary Note 2). We solved this equation using the QR factorization algorithm.

Ethics

Human experiments complied with all relevant ethical regations. The Helsinki Ethics Committee of Hadassah Hospital, Jerusalem, Israel approved the experimental procedure. Written informed consent was obtained from each participant prior to the procedure.

Human subjects

Human measurements were performed on 23 young adults (aged 27 ± 2 years, 11 females), and 18 older adults (aged 67 ± 6 years, five females). Healthy volunteers were recruited from the community surrounding the Hebrew University of Jerusalem.

MRI acquisition for human subjects

Data was collected on a 3 T Siemens MAGNETOM Skyra scanner equipped with a 32-channel head receive-only coil at the ELSC neuroimaging unit at the Hebrew University.

For quantitative R1, R2*, & MTV mapping, 3D Spoiled gradient (SPGR) echo images were acquired with different flip angles (α = 4°, 10°, 20°, and 30°). Each image included five equally spaced echoes (TE = 3.34–14.02 ms) and the TR was 19 ms (except for six young subjects for which the scan included only one TE = 3.34 ms). The scan resolution was 1 mm isotropic. For calibration, we acquired additional spin-echo inversion recovery scan with an echo-planar imaging (EPI) read-out (SEIR-epi). This scan was done with a slab-inversion pulse and spatial-spectral fat suppression. For SEIR-epi, the TE/TR was 49/2920 ms. TI were 200, 400, 1,200, and 2400 ms. We used 2-mm in-plane resolution with a slice thickness of 3 mm. The EPI read-out was performed using 2 × acceleration.

For quantitative T2 mapping, multi‐SE images were acquired with ten equally spaced spin echoes between 12 ms and 120 ms. The TR was 4.21 s. The scan resolution was 2 mm isotropic. T2 scans of four subjects (one young, three old) were excluded from the analysis due to motion.

For quantitative MTsat mapping, 3D Spoiled gradient (SPGR) echo image were acquired with an additional MT pulse. The flip angle was 10°, the TE/TR was 3.34/27 ms. The scan resolution was 1 mm isotropic.

Whole-brain DTI measurements were performed using a diffusion-weighted spin-echo EPI sequence with isotropic 1.5-mm resolution. Diffusion weighting gradients were applied at 64 directions and the strength of the diffusion weighting was set to b = 2000 s/mm2 (TE/TR = 95.80/6000 ms, G = 45mT/m, δ = 32.25 ms, Δ = 52.02 ms). The data includes eight non-diffusion-weighted images (b = 0). In addition, we collected non-diffusion-weighted images with reversed phase-encode blips. For five subjects (four young, one old) we failed to acquire this correction data and they were excluded from the diffusion analysis.

Anatomical images were acquired with 3D magnetization prepared rapid gradient echo (MP-RAGE) scans for 24 of the subjects (14 from the younger subjects, 10 from the older subjects). The scan resolution was 1 mm isotropic, the TE/TR was 2.98/2300 ms. Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) scans were acquired for the rest of the subjects. The scan resolution was 1 mm isotropic, the TE/TR was 2.98/5000 ms.

Estimation of qMRI parameters for human subjects

Whole-brain MTV and R1 maps, together with bias correction maps of B1 + and B1-, were computed using the mrQ software39,69 (https://github.com/mezera/mrQ). Voxels in which the B1 + inhomogeneities were extrapolated and not interpolated were removed from the MTV and R1 maps. While we did not correct our MTV estimates for R2*, we showed that employing such a correction does not significantly change our results (see Supplementary Note 6, Supplementary Figs. 20–27). MTV maps of four subjects had bias in the lower part of the brain and they were therefore excluded from the analysis presented in Fig. 3, which includes ROIs in the brainstem.

Whole-brain T2 maps were computed by implementing the echo‐modulation curve (EMC) algorithm68. To combine the MTV and T2 we co-registered the quantitative MTV map to the T2 map. We used the ANTS software package70 to calculate the transformation and to warp the MTV map and the segmentation. The registration was computed to match the T1 map to the T2 map. Next, we applied the calculated transformation to MTV map (since MTV and T1 are in the same imaging space) and resampled the MTV map to match the resolution of the T2 map. The same transformation was also applied to the segmentation. R2 maps were calculated as 1/T2.

Whole-brain MTsat maps were computed as described in Helms et al.37. The MTsat measurement was extracted from Eq. (1):

MTsat=𝑀0𝐵1𝛼𝑅1TR𝑆MT−(𝐵1𝛼)22−𝑅1TR
(1)
Where SMT is the signal of the SPGR scan with additional MT pulse, α is the flip angle and TR is the repetition time. Mo (the equilibrium magnetization parameter), B1 (the transmit inhomogeneity), and R1 estimations were computed from the non-MT weighted SPGR scans, during the pipeline described under “MTV & R1 estimation”. Registration of the SMT image to the imaging space of the MTV map was done using a rigid-body alignment (R1, B1, and MO are all in the same space as MTV).

Diffusion analysis was done using the FDT toolbox in FSL71,72. Susceptibility and eddy current induced distortions were corrected using the reverse phase-encode data, with the eddy and topup commands73,74. MD maps were calculated using vistasoft (https://github.com/vistalab/vistasoft/wiki). We used a rigid-body alignment to register the corrected dMRI data to the imaging space of the MTV map (Flirt, FSL). In order to calculate the MD-MTV derivatives, we resampled the MTV map and the segmentation to match the dMRI resolution.

We used the SPGR scans with multiple echoes to estimate R2*. Fitting was done through the MPM toolbox75. As we had four SPGR scans with variable flip angles, we averaged the R2* maps acquired from each of these scans for increased SNR.

Human brain segmentation

Whole-brain segmentation was computed automatically using the FreeSurfer segmentation algorithm76. For subjects who had an MP-RAGE scan, we used it as a reference. For the other subjects the MP2RAGE scan was used as a reference. These anatomical images were registered to the MTV space prior to the segmentation process, using a rigid-body alignment. Sub-cortical gray-matter structures were segmented with FSL’s FIRST tool77. To avoid partial volume effects, we removed the outer shell of each ROI and left only the core.

MDM computation in the human brain

We computed the dependency of each qMRI parameter (R1, MTsat, MD, and R2) on MTV in different brain areas. This process was implemented in MATLAB (MathWorks, Natwick, MI, USA). For each ROI, we extracted the MTV values from all voxels and pooled them into 36 bins spaced equally between 0.05 and 0.40. This was done so that the linear fit would not be heavily affected by the density of the voxels in different MTV values. We removed any bins in which the number of voxels was smaller than 4% of the total voxel count in the ROI. The median MTV of each bin was computed, along with the median of the qMRI parameter. We used these data points to fit the linear model across bins using Eq. (2):

qMRIparameters=𝑎∗MTV+𝑏
(2)
The slope of this linear model (“a”) represents the MTV derivative of the linear equation. We used this derivative estimate to compute the MDM signatures.

For each subject, ROIs in which the total voxel count was smaller than a set threshold of 500 voxels for the MTsat and R1 maps, 150 voxels for the MD map, and 50 voxels for the R2 map were excluded.

Principal component analysis (PCA) in the human brain

To estimate the variability in the MDM signatures across the brain, we computed the first principal component (PC) of MDM. For each MDM dimension (MTV derivatives of R1, MTsat, MD, and R2), we evaluated the median of the different brain areas across the young subjects. As each MDM dimension has different units, we then computed the z-score of each dimension across the different brain area. Finally, we performed PCA. The variables in this analysis were the different MDM dimensions, and the observations were the different brain areas. From this analysis, we derived the first PC that accounts for most of the variability in MDM signatures across the brain. To estimate the median absolute deviations (MAD) across subjects of each MDM measurement in the PC basis, we applied the z-score transformation to the original MAD and then projected them onto the PC basis.

To compute the first PC of standard qMRI parameters we followed the same procedure, but used R1, MTsat, MD, and R2 instead of their MTV derivatives.

For the first PC of molecular composition, we followed the same procedure, but used the phospholipid composition and the ratio between phospholipids to proteins and cholesterol as variables. The data was taken from eight post-mortem human brains7. Brains were obtained from individuals between 54 and 57 years of age, which were autopsied within 24 h after death.

Linear model for prediction of human molecular composition

We used MDM measurements in order to predict the molecular composition of different brain areas (Fig. 3c). For this analysis we used Supplementary Eq. 5 in the Supplementary Note 2. We solved this equation using QR factorization algorithm (for more details see Supplementary Note 3).

Gene-expression dataset

For the gene-expression analysis we followed the work of Ben-David and Shifman46. Microarray data was acquired from the Allen Brain Atlas (http://human.brain-map.org/well_data_files) and included a total of 1340 microarray profiles from donors H0351.2001 and H0351.2002, encompassing the different regions of the human brain. The donors were 24 and 39 years old, respectively, at the time of their death, with no known psychopathologies. We used the statistical analysis described by Ben-David and Shifman46. They constructed a gene network using a weighted gene co-expression network analysis. The gene network included 19 modules of varying sizes, from 38 to 7385 genes. The module eigengenes were derived by taking the first PC of the expression values in each module. In addition, we used the gene ontology enrichment analysis described by Ben-David and Shifman to define the name of each module. The colors of the different modules in the Fig. 4 and Supplementary Fig. 10 are the same as in the original paper.

Next, we matched between the gene-expression data and the MRI measurements. This analysis was done on 35 cortical regions extracted from FreeSurfer cortical parcellation. We downloaded the T1-weighted images of the two donors provided by the Allen Brain Atlas (http://human.brain-map.org/mri_viewers/data) and used them as a reference for FreeSurfer segmentation. We then found the FreeSurfer label of each gene-expression sample using the sample’s coordinates in brain space. We removed samples for which the FreeSurfer label and the label provided in the microarray dataset did not agree (there were 72 such samples out of 697 cortical samples). For each gene module, we averaged over the eigengenes of all samples from the same cortical area across the two donors.

Last, we compared the cortical eigengene of each module to the projection of cortical areas on the first PC of MDM. In addition, we compared the modules’ eigengenes to the MTV values of the cortical areas and to the projection of cortical areas on the first PC of standard qMRI parameters (Supplementary Fig. 10). These 57 correlations were corrected for multiple comparisons using the FDR method.

Brain region’s volume computation

To estimate the volume of different brain regions, we calculated the number of voxels in the FreeSurfer segmentation of each region (see “Brain segmentation”).

R2* correction for MTV
To correct the MTV estimates for R2* we used Eq. (3):

MTV𝐶=1−(1−MTV)⋅exp(TE⋅R2∗)
(3)
Where MTVC is the corrected MTV.

Statistical analysis

The statistical significance of the differences between the age groups was computed using an independent-sample t-test (alpha = 0.05, both right and left tail) and was corrected for multiple comparisons using the false-discovery rate (FDR) method. For this analysis, MRI measurements of both hemispheres of bilateral brain regions were joined together. R2 measurements were adjusted for the number of data points. All statistical tests were two-sided.

Post-mortem tissue acquisition

Two post-mortem porcine brains were purchased from BIOTECH FARM.

Post-mortem MRI acquisition

Brains were scanned fresh (without fixation) in water within 6 h after death. Data was collected on a 3 T Siemens MAGNETOM Skyra scanner equipped with a 32-channel head receive-only coil at the ELSC neuroimaging unit at the Hebrew University.

For quantitative R1, R2*, & MTV mapping, 3D Spoiled gradient (SPGR) echo images were acquired with different flip angles (α = 4°, 10°, 20°, and 30°). Each image included five equally spaced echoes (TE = 4.01 – 16.51 ms) and the TR was 22 ms. The scan resolution was 0.8 mm isotropic. For calibration, we acquired additional spin-echo inversion recovery scan with an echo-planar imaging (EPI) read-out (SEIR-epi). This scan was done with a slab-inversion pulse and spatial-spectral fat suppression. For SEIR-epi, the TE/TR was 49/2920 ms. TI were 50, 200, 400, 1200 ms. The scan resolution was 2 mm isotropic. The EPI read-out was performed using 2 × acceleration.

For quantitative T2 mapping, multi‐SE images were acquired with ten equally spaced spin echoes between 12 and 120 ms. The TR was 4.21 s. The scan resolution was 2 mm isotropic.

For quantitative MTsat mapping, 3D Spoiled gradient (SPGR) echo image were acquired with an additional MT pulse. The flip angle was 10°, the TE/TR was 4.01/40 ms. The scan resolution was 0.8 mm isotropic.

Whole-brain DTI measurements were performed using a diffusion-weighted spin-echo EPI sequence with isotropic 1.5-mm resolution. Diffusion weighting gradients were applied at 64 directions and the strength of the diffusion weighting was set to b = 2000 s/mm2 (TE/TR = 95.80/6000 ms, G = 45mT/m, δ = 32.25 ms, Δ = 52.02 ms). The data includes eight non-diffusion-weighted images (b = 0).

For anatomical images, 3D magnetization prepared rapid gradient echo (MP-RAGE) scans were acquired. The scan resolution was 1 mm isotropic, the TE/TR was 2.98/2300 ms.

Histological analysis

Following the MRI scans the brains were dissected. Total of 42 brain regions were identified. Four samples were excluded as we were not able to properly separate the WM from the GM. One sample was excluded as we could not properly identify its anatomical origin. Additional two samples were too small for TLC analysis.

The non-water fraction (MTV) was determined by desiccation, also known as the dry-wet method. A small fraction of each brain sample (~0.25 g) was weighed. In order to completely dehydrate the fresh tissues, they were left for several days in a vacuum dessicator over silica gel at 4 °C. The experiment ended when no further weight loss occurred. The MTV of each brain sample was calculated based on the difference between the wet (Wwet) and dry (Wdry) weights of the tissue (Eq. 4):

MTV=𝑊wet−𝑊dry𝑊wet
(4)
For lipid extraction and lipidomics analysis78, Brain samples were weighted and homogenized with saline in plastic tubes on ice at concentration of 1 mg/12.5 µL. Two-hundred fifty microliters from each homogenate were utilized for lipid extraction and analysis with thin-layer chromatography (TLC). The lipid species distribution was analyzed by TLC applying 150 µg aliquots. Samples were reconstituted in 10 µL of Folch mixture and spotted on Silica-G TLC plates. Standards for each fraction were purchased from Sigma Aldrich (Rehovot, Israel) and were spotted in separate TLC lanes, i.e., 50 µg of triacylglycerides (TG), cholesterol (Chol), cholesteryl esters (CE), free fatty acids (FFA), lysophospholipids (Lyso), sphingomyelin (Spg), phosphatidylcholine (PtdCho), phosphatidylinositol (PI), phosphatidylserine (PS), and phosphatidylethanolamine (PE). Plates were then placed in a 20 × 20 cm TLC chamber containing petroleum ether, ethyl ether, and acetic acid (80:20:1, v/v/v) for quantification of neutral lipids or chloroform, methanol, acetic acid, and water (65:25:4:2, v:v:v:v) for quantification of polar lipids and run for 45 min. TG, Chol, CE, FFA, phospholipids (PL), Lyso, Spg, PtdCho, PI, PS, and PE bands were visualized with Iodine, scanned and quantified by Optiquant after scanning (Epson V700). Lyso, CE, TG, and PI were excluded from further analysis as their quantification was noisy and demonstrated high variability across TLC plates. This analysis was conducted under the guidance of Prof. Alicia Leikin-Frenkel in the Bert Strassburger Lipid Center, Sheba, Tel Hashomer.

Estimation of qMRI parameters in the post-mortem brain

Similar to human subjects.

Brain segmentation of post-mortem brain

Brain segmentation was done manually. Five tissue samples were excluded as we could not identify their origin location in the MRI scans.

MDM computation in the post-mortem brain

We computed the dependency of each qMRI parameter (R1, MTsat, MD, and R2) on MTV in different brain areas similarly to the analysis of the human subjects.

Principal component analysis (PCA) in the post-mortem brain

To estimate the variability in the MDM signatures across the brain, we computed the first principal component (PC) of MDM. PCA analysis was performed with four variables corresponding to the MDM dimensions (MTV derivatives of R1, MTsat, MD, and R2), and 30 observations corresponding to the different brain regions. As each MDM dimension has different units, we first computed the z-score of each dimension across the different brain areas prior to the PCA. From this analysis we derived the first PC that accounts for most of the variability in MDM signatures across the brain.

To compute the first PC of standard qMRI parameters we followed the same procedure, but used R1, MTsat, MD, and R2 instead of their MTV derivatives.

To estimate the variability in the lipid composition across the brain, we computed the first principal component (PC) of lipidomics. PCA analysis was performed with seven variables corresponding to the different polar and neutral lipids (Chol, FFA, PL, Spg, PtdCho, PS, PE), and 30 observations corresponding to the different brain regions. From this analysis, we derived the first PC that accounts for most of the variability in lipid composition across the brain.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

A toolbox for computing MDM signatures is available at [https://github.com/MezerLab/MDM_toolbox].

The code generating the figures of in the paper is available at [https://github.com/MezerLab/MDM_Gen_Figs].

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Acknowledgements

This work was supported by the ISF grant 0399306, awarded to A.A.M. We acknowledge Ady Zelman for the assistance in collecting the human MRI data. We thank Assaf Friedler for assigning research lab space and advising on the lipid sample experiments. We thank Inbal Goshen for assigning research lab space and advising on the protein and ion samples as well as the porcine brain experiments. We thank Magnus Soderberg for advising on histological data interpretation. We are grateful to Brian A. Wandell, Jason Yeatman, Hermona Soreq, Ami Citri, Mark Does, Yaniv Ziv, Ofer Yizhar, Shai Berman, Roey Schurr, Jonathan Bain, Asier Erramuzpe Aliaga, Menachem Gutman, and Esther Nachliel for their critical reading of the manuscript and very useful comments. We thank Prof. Alicia Leikin-Frenkel for her guidance with the TLC analysis. We thank Rona Shaharabani for guidance and support in the post-mortem experiments.

Affiliations

The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
Shir Filo, Oshrat Shtangel, Noga Salamon, Adi Kol, Batsheva Weisinger & Aviv A. Mezer
Department of Genetics, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
Sagiv Shifman
Contributions
S.F., O.S., and A.A.M. conceived of the presented idea. S.F. and A.A.M. wrote the manuscript and designed the figures. S.F. collected the human and non-human brain datasets and analyzed them. O.S. performed the phantom experiments and analyzed them. B.W. performed the phantom experiments for non-lipid compounds. N.S. performed the gene-expression analysis. S.S. assisted and instructed with the gene-expression analysis. A.K. performed the porcine brain dissection.

Corresponding author

Correspondence to Aviv A. Mezer.

Ethics declarations & Competing interests

A.A.M, S.F., O.S. and the Hebrew University of Jerusalem have filed a patent application describing the technology used to measure MDM in this work. The other authors declare no competing interests.

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Artificial intelligence can be a useful tool to predict Alzheimer

Reporter: Irina Robu, PhD

The Alzheimer’s Association estimate that around 5.7 million people live with Alzheimer’s disease in the United States which will rise to almost 14 million by 2050. Earlier diagnosis would not only benefit those affected, but it could also jointly save about $7.9 trillion in medical care and related costs over time. As Alzheimer’s disease progresses, it changes how brain cells use glucose. This alteration in glucose metabolism shows up in a type of PET imaging that tracks the uptake of a radioactive form of glucose called 18F-fluorodeoxyglucose. By giving instructions about what to look for, the scientists were able to train the deep learning algorithm to assess the PET images for early signs of Alzheimer’s.
The researchers from University of California San Francisco used positron-emission tomography images of 1002 people’s brain to train the deep learning algorithm they developed. They used 90 percent of images to teach the algorithm to spot features of Alzheimer’s disease and the remaining 10 percent to verify its performance. The researchers tested the algorithm on PET images of brains from 40 people, from which they were able to predict which individuals would receive a final diagnosis of Alzheimer’s. On average, the people who were tested were diagnosed with the disease more than 6 years after the scans.
According to the Radiology journal in which the research was published, the team describes how the algorithm “achieved 82 percent specificity at 100 percent sensitivity, an average of 75.8 months prior to the final diagnosis.” The researchers taught the algorithm with the help of more than 2,109 PET images of 1,002 individuals’ brains. The algorithm uses deep learning, which allows the algorithm to “teach itself” what to look for by spotting subtle differences among the thousands of images. The algorithm was as good as, if not better than, human experts at analyzing the FDG PET images.
Future advances will involve using larger data sets and additional images taken over time from people at various clinics and institutions. In the future, the algorithm could be a beneficial addition to the radiologist’s toolbox and advance opportunities for the early treatment of Alzheimer’s disease.

Source

https://www.medicalnewstoday.com/articles/323608.php

 

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Alzheimer’s Disease: Novel Therapeutical Approaches — Articles of Note @PharmaceuticalIntelligence.com

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

 

The Rogue Immune Cells That Wreck the Brain

Beth Stevens thinks she has solved a mystery behind brain disorders such as Alzheimer’s and schizophrenia.

by Adam Piore   April 4, 2016            

https://www.technologyreview.com/s/601137/the-rogue-immune-cells-that-wreck-the-brain/

Microglia are part of a larger class of cells—known collectively as glia—that carry out an array of functions in the brain, guiding its development and serving as its immune system by gobbling up diseased or damaged cells and carting away debris. Along with her frequent collaborator and mentor, Stanford biologist Ben Barres, and a growing cadre of other scientists, Stevens, 45, is showing that these long-overlooked cells are more than mere support workers for the neurons they surround. Her work has raised a provocative suggestion: that brain disorders could somehow be triggered by our own bodily defenses gone bad.

In one groundbreaking paper, in January, Stevens and researchers at the Broad Institute of MIT and Harvard showed that aberrant microglia might play a role in schizophrenia—causing or at least contributing to the massive cell loss that can leave people with devastating cognitive defects. Crucially, the researchers pointed to a chemical pathway that might be targeted to slow or stop the disease. Last week, Stevens and other researchers published a similar finding for Alzheimer’s.

This might be just the beginning. Stevens is also exploring the connection between these tiny structures and other neurological diseases—work that earned her a $625,000 MacArthur Foundation “genius” grant last September.

All of this raises intriguing questions. Is it possible that many common brain disorders, despite their wide-ranging symptoms, are caused or at least worsened by the same culprit, a component of the immune system? If so, could many of these disorders be treated in a similar way—by stopping these rogue cells?

VIEW VIDEO

Science  31 Mar 2016;        http://dx.doi.org:/10.1126/science.aad8373      Complement and microglia mediate early synapse loss in Alzheimer mouse models.
Soyon Hong1, Victoria F. Beja-Glasser1,*, Bianca M. Nfonoyim1,*,…., Ben A. Barres6, Cynthia A. Lemere,2, Dennis J. Selkoe2,7, Beth Stevens1,8,

Synapse loss in Alzheimer’s disease (AD) correlates with cognitive decline. Involvement of microglia and complement in AD has been attributed to neuroinflammation, prominent late in disease. Here we show in mouse models that complement and microglia mediate synaptic loss early in AD. C1q, the initiating protein of the classical complement cascade, is increased and associated with synapses before overt plaque deposition. Inhibition of C1q, C3 or the microglial complement receptor CR3, reduces the number of phagocytic microglia as well as the extent of early synapse loss. C1q is necessary for the toxic effects of soluble β-amyloid (Aβ) oligomers on synapses and hippocampal long-term potentiation (LTP). Finally, microglia in adult brains engulf synaptic material in a CR3-dependent process when exposed to soluble Aβ oligomers. Together, these findings suggest that the complement-dependent pathway and microglia that prune excess synapses in development are inappropriately activated and mediate synapse loss in AD.

Genome-wide association studies (GWAS) implicate microglia and complement-related pathways in AD (1). Previous research has demonstrated both beneficial and detrimental roles of complement and microglia in plaque-related neuropathology (23); however, their roles in synapse loss, a major pathological correlate of cognitive decline in AD (4), remain to be identified. Emerging research implicates microglia and immune-related mechanisms in brain wiring in the healthy brain (1). During development, C1q and C3 localize to synapses and mediate synapse elimination by phagocytic microglia (57). We hypothesized that this normal developmental synaptic pruning pathway is activated early in the AD brain and mediates synapse loss.

Scientists have known about glia for some time. In the 1800s, the pathologist Rudolf Virchow noted the presence of small round cells packing the spaces between neurons and named them “nervenkitt” or “neuroglia,” which can be translated as nerve putty or glue. One variety of these cells, known as astrocytes, was defined in 1893. And then in the 1920s, the Spanish scientist Pio del Río Hortega developed novel ways of staining cells taken from the brain. This led him to identify and name two more types of glial cells, including microglia, which are far smaller than the others and are characterized by their spidery shape and multiple branches. It is only when the brain is damaged in adulthood, he suggested, that microglia spring to life—rushing to the injury, where it was thought they helped clean up the area by eating damaged and dead cells. Astrocytes often appeared on the scene as well; it was thought that they created scar tissue.

This emergency convergence of microglia and astrocytes was dubbed “gliosis,” and by the time Ben Barres entered medical school in the late 1970s, it was well established as a hallmark of neurodegenerative diseases, infection, and a wide array of other medical conditions. But no one seemed to understand why it occurred. That intrigued Barres, then a neurologist in training, who saw it every time he looked under a microscope at neural tissue in distress. “It was just really fascinating,” he says. “The great mystery was: what is the point of this gliosis? Is it good? Is it bad? Is it driving the disease process, or is it trying to repair the injured brain?”

Barres began looking for the answer. He learned how to grow glial cells in a dish and apply a new recording technique to them. He could measure their electrical qualities, which determine the biochemical signaling that all brain cells use to communicate and coördinate activity.

Barres’s group had begun to identify the specific compounds astrocytes secreted that seemed to cause neurons to grow synapses. And eventually, they noticed that these compounds also stimulated production of a protein called C1q.

Conventional wisdom held that C1q was activated only in sick cells—the protein marked them to be eaten up by immune cells—and only outside the brain. But Barres had found it in the brain. And it was in healthy neurons that were arguably at their most robust stage: in early development. What was the C1q protein doing there?

https://d267cvn3rvuq91.cloudfront.net/i/images/glia33.jpg?sw=590&cx=0&cy=0&cw=2106&ch=2106

A stained astrocyte.

The answer lies in the fact that marking cells for elimination is not something that happens only in diseased brains; it is also essential for development. As brains develop, their neurons form far more synaptic connections than they will eventually need. Only the ones that are used are allowed to remain. This pruning allows for the most efficient flow of neural transmissions in the brain, removing noise that might muddy the signal.

Kalaria, RN. Microglia and Alzheimer’s disease. Current Opinion in Hematology: January 1999 – Volume 6 – Issue 1 – p 15

Microglia play a major role in the cellular response associated with the pathological lesions of Alzheimer’s disease. As brain-resident macrophages, microglia elaborate and operate under several guises that seem reminiscent of circulating and tissue monocytes of the leucocyte repertoire. Although microglia bear the capacity to synthesize amyloid β, current evidence is most consistent with their phagocytic role. This largely involves the removal of cerebral amyloid and possibly the transformation of amyloid β into fibrils. The phagocytic functions also encompass the generation of cytokines, reactive oxygen and nitrogen species, and various proteolytic enzymes, events that may exacerbate neuronal damage rather than incite outgrowth or repair mechanisms. Microglia do not appear to function as true antigen-presenting cells. However, there is circumstantial evidence that suggests functional heterogeneity within microglia. Pharmacological agents that suppress microglial activation or reduce microglial-mediated oxidative damage may prove useful strategies to slow the progression of Alzheimer’s disease.

Streit WJ. Microglia and Alzheimer’s disease pathogenesis. J Neurosci Res 1 July 2004; 77(1):1–8
http://dx.doi.org:/10.1002/jnr.20093

The most visible and, until very recently, the only hypothesis regarding the involvement of microglial cells in Alzheimer’s disease (AD) pathogenesis is centered around the notion that activated microglia are neurotoxin-producing immune effector cells actively involved in causing the neurodegeneration that is the cause for AD dementia. The concept of detrimental neuroinflammation has gained a strong foothold in the AD arena and is being expanded to other neurodegenerative diseases. This review takes a comprehensive and critical look at the overall evidence supporting the neuroinflammation hypothesis and points out some weaknesses. The current work also reviews evidence for an alternative theory, the microglial dysfunction hypothesis, which, although eliminating some of the shortcomings, does not necessarily negate the amyloid/neuroinflammation theory. The microglial dysfunction theory offers a different perspective on the identity of activated microglia and their role in AD pathogenesis taking into account the most recent insights gained from studying basic microglial biology.

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Kira Irving MosherabTony Wyss-Corayac. Microglial dysfunction in brain aging and Alzheimer’s disease.

Review – Part of the Special Issue: Alzheimer’s Disease – Amyloid, Tau and Beyond. Biochemical Pharmacology 15 Apr 2014; 88(4):594–604   doi:10.1016/j.bcp.2014.01.008

Microglia, the immune cells of the central nervous system, have long been a subject of study in the Alzheimer’s disease (AD) field due to their dramatic responses to the pathophysiology of the disease. With several large-scale genetic studies in the past year implicating microglial molecules in AD, the potential significance of these cells has become more prominent than ever before. As a disease that is tightly linked to aging, it is perhaps not entirely surprising that microglia of the AD brain share some phenotypes with aging microglia. Yet the relative impacts of both conditions on microglia are less frequently considered in concert. Furthermore, microglial “activation” and “neuroinflammation” are commonly analyzed in studies of neurodegeneration but are somewhat ill-defined concepts that in fact encompass multiple cellular processes. In this review, we have enumerated six distinct functions of microglia and discuss the specific effects of both aging and AD. By calling attention to the commonalities of these two states, we hope to inspire new approaches for dissecting microglial mechanisms.

http://ars.els-cdn.com/content/image/1-s2.0-S000629521400032X-fx1.jpg

 

A Olmos-Alonso, STT Schetters, S Sri, K Askew, …, VH Perry, D Gomez-Nicola.
Pharmacological targeting of CSF1R inhibits microglial proliferation and prevents the progression of Alzheimer’s-like pathology. Brain 8 Jan 2016.  http://dx.doi.org/10.1093/brain/awv379

The proliferation and activation of microglial cells is a hallmark of several neurodegenerative conditions. This mechanism is regulated by the activation of the colony-stimulating factor 1 receptor (CSF1R), thus providing a target that may prevent the progression of conditions such as Alzheimer’s disease. However, the study of microglial proliferation in Alzheimer’s disease and validation of the efficacy of CSF1R-inhibiting strategies have not yet been reported. In this study we found increased proliferation of microglial cells in human Alzheimer’s disease, in line with an increased upregulation of the CSF1R-dependent pro-mitogenic cascade, correlating with disease severity. Using a transgenic model of Alzheimer’s-like pathology (APPswe, PSEN1dE9; APP/PS1 mice) we define a CSF1R-dependent progressive increase in microglial proliferation, in the proximity of amyloid-β plaques. Prolonged inhibition of CSF1R in APP/PS1 mice by an orally available tyrosine kinase inhibitor (GW2580) resulted in the blockade of microglial proliferation and the shifting of the microglial inflammatory profile to an anti-inflammatory phenotype. Pharmacological targeting of CSF1R in APP/PS1 mice resulted in an improved performance in memory and behavioural tasks and a prevention of synaptic degeneration, although these changes were not correlated with a change in the number of amyloid-β plaques. Our results provide the first proof of the efficacy of CSF1R inhibition in models of Alzheimer’s disease, and validate the application of a therapeutic strategy aimed at modifying CSF1R activation as a promising approach to tackle microglial activation and the progression of Alzheimer’s disease.

The neuropathology of Alzheimer’s disease shows a robust innate immune response characterized by the presence of activated microglia, with increased or de novo expression of diverse macrophage antigens (Akiyama et al., 2000; Edison et al., 2008), and production of inflammatory cytokines (Dickson et al., 1993; Fernandez-Botran et al., 2011). Evidence indicates that non-steroidal anti-inflammatory drugs (NSAIDs) protect from the onset or progression of Alzheimer’s disease (Hoozemans et al., 2011), suggestive of the idea that inflammation is a causal component of the disease rather than simply a consequence of the neurodegeneration. In fact, inflammation (Holmes et al., 2009), together with tangle pathology (Nelson et al., 2012) or neurodegeneration-related biomarkers (Wirth et al., 2013) correlate better with cognitive decline than amyloid-b accumulation, but the underlying mechanisms of the sequence of events that contribute to the clinical symptoms are poorly understood. The contribution of inflammation to disease pathogenesis is supported by recent genome-wide association studies, highlighting immune-related genes such as CR1 (Jun et al., 2010), TREM2 (Guerreiro et al., 2013; Jonsson et al., 2013) or HLA-DRB5–HLA-DRB1 in association with Alzheimer’s disease (European Alzheimer’s Disease et al., 2013). Additionally, a growing body of evidence suggests that systemic inflammation may interact with the innate immune response in the brain to act as a ‘driver’ of disease progression and exacerbate symptoms (Holmes et al., 2009, 2011). Microglial cells are the master regulators of the neuroin- flammatory response associated with brain disease (GomezNicola and Perry, 2014a, b). Activated microglia have been demonstrated in transgenic models of Alzheimer’s disease (LaFerla and Oddo, 2005; Jucker, 2010) and have been recently shown to dominate the gene expression landscape of patients with Alzheimer’s disease (Zhang et al., 2013). Recently, microglial activation through the transcription factor PU.1 has been reported to be capital for the progression of Alzheimer’s disease, highlighting the role of microglia in the disease-initiating steps (Gjoneska et al., 2015). Results from our group, using a murine model of chronic neurodegeneration (prion disease), show large numbers of microglia with an activated phenotype (Perry et al., 2010) and a cytokine profile similar to that of Alzheimer’s disease (Cunningham et al., 2003). The expansion of the microglial population during neurodegeneration is almost exclusively dependent upon proliferation of resident cells (GomezNicola et al., 2013, 2014a; Li et al., 2013). An increased microglial proliferative activity has also been described in a mouse model of Alzheimer’s disease (Kamphuis et al., 2012) and in post-mortem samples from patients with Alzheimer’s disease (Gomez-Nicola et al., 2013, 2014b). This proliferative activity is regulated by the activation of the colony stimulating factor 1 receptor (CSF1R; GomezNicola et al., 2013). Pharmacological strategies inhibiting the kinase activity of CSF1R provide beneficial effects on the progression of chronic neurodegeneration, highlighting the detrimental contribution of microglial proliferation (Gomez-Nicola et al., 2013). The presence of a microglial proliferative response with neurodegeneration is also supported by microarray analysis correlating clinical scores of incipient Alzheimer’s disease with the expression of Cebpa and Spi1 (PU.1), key transcription factors controlling microglial lineage commitment and proliferation (Blalock et al., 2004). Consistent with these data, Csf1r is upregulated in mouse models of amyloidosis (Murphy et al., 2000), as well as in human post-mortem samples from patients with Alzheimer’s disease (Akiyama et al., 1994). Although these ideas would lead to the evaluation of the efficacy of CSF1R inhibitors in Alzheimer’s disease, we have little evidence regarding the level of microglial proliferation in Alzheimer’s disease or the effects of CSF1R targeting in animal models of Alzheimer’s disease-like pathology. In this study, we set out to define the microglial proliferative response in both human Alzheimer’s disease and a mouse model of Alzheimer’s disease-like pathology, as well as the activation of the CSF1R pathway. We provide evidence for a consistent and robust activation of a microglial proliferative response, associated with the activation of CSF1R. We provide proof-of-target engagement and efficacy of an orally available CSF1R inhibitor (GW2580), which inhibits microglial proliferation and partially prevents the pathological progression of Alzheimer’s disease-like pathology, supporting the evaluation of CSF1R-targeting approaches as a therapy for Alzheimer’s disease.

Post-mortem samples of Alzheimer’s disease For immunohistochemical analysis, human brain autopsy tissue samples (temporal cortex, paraffin-embedded, formalin- fixed, 96% formic acid-treated, 6-mm sections) from the National CJD Surveillance Unit Brain Bank (Edinburgh, UK) were obtained from cases of Alzheimer’s disease (five females and five males, age 58–76) or age-matched controls (four females and five males, age 58–79), in whom consent for use of autopsy tissues for research had been obtained. All cases ful- filled the criteria for the pathological diagnosis of Alzheimer’s disease. Ethical permission for research on autopsy materials stored in the National CJD Surveillance Unit was obtained from Lothian Region Ethics Committee

Figure 1 Characterization of the microglial proliferative response in Alzheimer’s disease. (A–C) Immunohistochemical analysis and quantification of the number of total microglial cells (Iba1+ ; A) or proliferating microglial cells (Iba1+Ki67 + ; B) in the grey (GM) and white matter (WM) of the temporal cortex of Alzheimer’s disease cases (AD) and age-matched non-demented controls (NDC). (C) Representative pictures of the localization of a marker of proliferation (Ki67, dark blue) in microglial cells (Iba1+ , brown) in the grey matter of the temporal cortex of non-demented controls or Alzheimer’s disease cases. (D) RT-PCR analysis of the mRNA expression of CSF1R, CSF1, IL34, SPI1 (PU.1), CEBPA, RUNX1 and PCNA in the temporal cortex of Alzheimer’s disease cases and age-matched non-demented controls. Expression of mRNA represented as mean SEM and indicated as relative expression to the normalization factor (geometric mean of four housekeeping genes; GAPDH, HPRT, 18S and GUSB) using the 2-CT method. Statistical differences: *P 50.05, **P 50.01, ***P 50.001. Data were analysed with a two-way ANOVA and a post hoc Tukey test (A and B) or with a two-tailed Fisher t-test (D). Scale bar in C = 50 mm.

Increased microglial proliferation and CSF1R activity are closely associated with the progression of Alzheimer’s disease-like pathology 

Pharmacological targeting of CSF1R activation with an orally-available inhibitor blocks microglial proliferation in APP/PS1 mice

CSF1R inhibition prevents the progression of Alzheimer’s disease-like pathology

The innate immune component has a clear influence over the onset and progression of Alzheimer’s disease. The analysis of therapeutic approaches aimed at controlling neuroinflammation in Alzheimer’s disease is moving forward at the preclinical and clinical level, with several clinical trials aimed at modulating inflammatory components of the disease. We have previously demonstrated that the proliferation of microglial cells is a core component of the neuroinflammatory response in a model of prion disease, another chronic neurodegenerative disease, and is controlled by the activation of CSF1R (Gomez-Nicola et al., 2013). This aligns with recent reports pinpointing the causative effect of the activation of the microglial proliferative response on the neurodegenerative events of human and mouse Alzheimer’s disease, highlighting the activity of the master regulator PU.1 (Gjoneska et al., 2015). Our results provide a proof of efficacy of CSF1R inhibition for the blockade of microglial proliferation in a model of Alzheimer’s disease-like pathology. Treatment with the orally available CSF1R kinase-inhibitor (GW2580) proves to be an effective disease-modifying approach, partially improving memory and behavioural performance, and preventing synaptic degeneration. These results support the previously reported link of the inflammatory response generated by microglia in models of Alzheimer’s disease with the observed synaptic and behavioural deficits, regardless of amyloid deposition (Jones and Lynch, 2014).

Our findings support the relevance of CSF1R signalling and microglial proliferation in chronic neurodegeneration and validate the evaluation of CSF1R inhibitors in clinical trials for Alzheimer’s disease. Our findings show that the inhibition of microglial proliferation in a model of Alzheimer’s disease-like pathology does not modify the burden of amyloid-b plaques, suggesting an uncoupling of the amyloidogenic process from the pathological progression of the disease.

 

Other Related Articles published in this Open Access Online Scientific Journal include the following:

Role of infectious agent in Alzheimer’s Disease?

Alzheimer’s disease, snake venome, amyloid and transthyretin

Alzheimer’s Disease – tau art thou, or amyloid

Breakthrough Prize for Alzheimer’s Disease 2016

Tau and IGF1 in Alzheimer’s Disease

Amyloid and Alzheimer’s Disease

Important Lead in Alzheimer’s Disease Model

BWH Researchers: Genetic Variations can Influence Immune Cell Function: Risk Factors for Alzheimer’s Disease,DM, and MS later in life

BACE1 Inhibition role played in the underlying Pathology of Alzheimer’s Disease

Late Onset of Alzheimer’s Disease and One-carbon Metabolism

Alzheimer’s Disease Conundrum – Are We Near the End of the Puzzle?

Ustekinumab New Drug Therapy for Cognitive Decline resulting from Neuroinflammatory Cytokine Signaling and Alzheimer’s Disease

New Alzheimer’s Protein – AICD

Developer of Alzheimer’s drug Exelon at Hebrew University’s School of Pharmacy: Israel Prize in Medicine awarded to Prof. Marta Weinstock-Rosin

TyrNovo’s Novel and Unique Compound, named NT219, selectively Inhibits the process of Aging and Neurodegenerative Diseases, without affecting Lifespan

@NIH – Discovery of Causal Gene Mutation Responsible for two Dissimilar Neurological diseases: Amyotrophic Lateral Sclerosis (ALS) and Frontotemporal Dementia (FTD)

Introduction to Nanotechnology and Alzheimer disease

Genomic Promise for Neurodegenerative Diseases, Dementias, Autism Spectrum, Schizophrenia, and Serious Depression

New ADNI Project to Perform Whole-genome Sequencing of Alzheimer’s Patients,

Brain Biobank

Removing Alzheimer plaques

Tracking protein expression

Schizophrenia genomics

Breakup of amyloid plaques

Mindful Discoveries

Beyond tau and amyloid

Serum Folate and Homocysteine, Mood Disorders, and Aging

Long Term Memory and Prions

Retromer in neurological disorders

Neurovascular pathways to neurodegeneration

Studying Alzheimer’s biomarkers in Down syndrome

Amyloid-Targeting Immunotherapy Targeting Neuropathologies with GSK33 Inhibitor

Brain Science

Sleep quality, amyloid and cognitive decline

microglia and brain maintenance

Notable Papers in Neurosciences

New Molecules to reduce Alzheimer’s and Dementia risk in Diabetic patients

The Alzheimer Scene around the Web

MRI Cortical Thickness Biomarker Predicts AD-like CSF and Cognitive Decline in Normal Adults

 

Keywords:

  • Alzheimer’s disease
  • microglia
  • gliosis
  • neurodegeneration
  • inflammation

 

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Role of infectious agent in Alzheimer’s Disease?

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Role of Infection in Alzheimer’s Ignored, Experts Say

Nancy A. Melville   http://www.medscape.com/viewarticle/860615

The potentially critical role of infection in the etiology of Alzheimer’s disease is largely neglected, despite decades of robust evidence from hundreds of human studies, as well as the possible therapeutic implications, experts say.

“Despite all the supportive evidence, the topic [of linking infections to Alzheimer’s disease] is often dismissed as ‘controversial,’ ” the authors of an editorial, signed by an international group of 33 researchers and clinicians, write.

The editorial was published online March 8 in theJournal of Alzheimer’s Disease.

Antiviral Treatment

“One recalls the widespread opposition initially to data showing that viruses cause some types of cancer, and that a bacterium causes stomach ulcers,” the authors write.

The implications could be just as important with regard to Alzheimer’s disease, coauthor Ruth F. Itzhaki, PhD, of the Faculty of Life Sciences at the University of Manchester, United Kingdom, toldMedscape Medical News.

“The implications are that patients could be treated with antiviral agents. These would not cure them, but might slow or even stop the progression of the disease,” she said.

The evidence points to herpes simplex virus type 1 (HSV1), Chlamydia pneumoniae, and several types of spirochetes, which make their way into the central nervous system (CNS), where they can remain in latent form indefinitely, the authors note.

The link with HSV1 is supported by as many as 100 studies. Only two studies oppose the association; both were published more than a decade ago, the authors state.

Under the prevailing theory, agents such as HSV1 undergo reactivation in the brain during aging and with the decline of the immune system, as well as when persons are under stress.

“The consequent neuronal damage ― caused by direct viral action and by virus-induced inflammation ― occurs recurrently, leading to (or acting as a cofactor for) progressive synaptic dysfunction, neuronal loss, and ultimately AD [Alzheimer’s disease],” the authors write

Importantly, that damage includes the induction of amyloid-β (Aβ) peptide deposits, considered a hallmark of Alzheimer’s disease, which initially appears to be only a defense mechanism, the authors add.

Causative Role?

In outlining some of the strongest evidence behind the theory, the authors note that although viruses and other microbes are common in the elderly brain and are usually dormant, influences such as stress and immunosuppression can cause reactivation.

“For example, HSV1 DNA is amplified in the brain of immunosuppressed patients,” they write.

In addition, herpes simplex encephalitis is known to damage regions of the CNS linked to the limbic system, and therefore to memory as well as cognitive and affective processes, the same regions affected in Alzheimer’s disease.

HSV infection is known to be significantly associated with the development of Alzheimer’s, and the disease is known to have a strong inflammatory component that is characteristic of infection, the authors say.

On a genetic level, research has shown that polymorphisms in the apolipoprotein E gene (APOE) that are linked to the risk for Alzheimer’s also control immune function and susceptibility to infectious disease.

In terms of evidence of a causative role of infection in Alzheimer’s disease, the authors cite studies indicating that brain infection, such as HIV or herpes virus, is linked to pathology similar to Alzheimer’s.

Notably, infection with HSV1 or bacteria in mice and cell culture studies has been shown to result in Aβ deposition and tau abnormalities typical of Alzheimer’s disease.

In addition, the olfactory dysfunction that is an early symptom of Alzheimer’s disease is consonant with a role of infection: The olfactory nerve leads to the lateral entorhinal cortex, where Alzheimer’s pathology spreads through the brain, and it is the likely portal of entry of HSV1 and other viruses into the brain, the authors note.

“Further, brainstem areas that harbor latent HSV directly irrigate these brain regions: brainstem virus reactivation would thus disrupt the same tissues as those affected in Alzheimer’s disease,” they write.

In terms of mechanisms, the authors cite mounting evidence that virus infection selectively upregulates the gene encoding cholesterol 25-hydroxylase (CH25H), and innate antiviral immunity is induced by its enzymatic product 25-hydroxycholesterol (25OHC).

The human CH25H polymorphisms control susceptibility to Alzheimer’s as well as Aβ deposition.

Consequently, “Aβ induction is likely to be among the targets of 25OHC, providing a potential mechanistic link between infection and Aβ production,” the authors write.

Considering the devastating toll Alzheimer’s disease takes on individual lives and society, the need to reconsider the collective evidence of a role for infection is pressing, the authors note.

“Alzheimer’s disease causes great emotional and physical harm to sufferers and their carers, as well as having enormously damaging economic consequences,” they write.

“Given the failure of the 413 trials of other types of therapy for Alzheimer’s disease carried out in the period 2002-2012, antiviral/antimicrobial treatment of Alzheimer’s disease patients, notably those who areAPOE ɛ4 carriers, could rectify the ‘no drug works’ impasse.

“We propose that further research on the role of infectious agents in Alzheimer’s disease causation, including prospective trials of antimicrobial therapy, is now justified.”

Chicken or the Egg?

Commenting on the editorial for Medscape Medical News, Richard B. Lipton, MD, Edwin S. Lowe Professor, vice chair of neurology, and director of the Division of Cognitive Aging and Dementia at Albert Einstein College of Medicine in New York City, applauded the effort to raise awareness of the issue.

“The authors are to be commended for reminding us of the hypothesis that infection may contribute to Alzheimer’s disease,” he told Medscape Medical News.

He noted the variety of genetic and environmental factors that can influence onset and progression of complex disorders such as Alzheimer’s disease.

“For Alzheimer’s disease, most people would agree that cardiovascular risk factors, traumatic brain injury, and stress increase risk of disease,” he said.

“It is entirely plausible that infectious agents may be one of many factors that contribute to the development of Alzheimer’s disease. Infectious agents could operate through several mechanisms.”

The evidence does not necessarily prove a causative role, he added.

“Temporality means that infection precedes disease,” he said. “The studies showing infectious and inflammatory markers in the Alzheimer’s brain don’t tell us which came first. Alzheimer’s disease could be a state which predisposes to infection.”

The editorialists’ financial disclosures are available online. Dr Lipton has disclosed no relevant financial relationships.

Microbes and Alzheimer’s Disease

KEY POINTS

  • Herpes simplex virus 1 (HSV-1) encephalitis predominantly involves the orbital surface of the frontal lobes and medial surface of the temporal lobes, resulting in areas of increased T2 signal on MRI
  • Herpes simplex virus 2 (HSV-2) is the primary cause of recurrent meningitis
  • After varicella, the varicella zoster virus (VZV) becomes latent in ganglia along the entire neuraxis; its reactivation can lead to herpes zoster, vasculopathy, myelitis, necrotizing retinitis or zoster sine herpete
  • The neurological complications of Epstein–Barr virus are diverse, and include meningitis, encephalitis, myelitis, radiculoneuropathy, and even autonomic neuropathy
  • The most common neurological complication of cytomegalovirus (CMV) is poly-radiculoneuropathy in immunocompromised individuals
  • Virological confirmation of neurological disease relies on the detection of herpesvirus-specific DNA in bodily fluids or tissues, herpesvirus-specific IgM in blood, or herpesvirus-specific IgM or IgG antibody in cerebrospinal fluid
  • HSV-1, HSV-2, VZV and CMV are the most treatable herpesviruses

Most HHVs can cause serious neurological disease of the PNS and CNS through primary infection or following virus reactivation from latently infected human ganglia or lymphoid tissue. The neurological complications include meningitis, encephalitis, myelitis, vasculopathy, acute and chronic radiculoneuritis, and various inflammatory diseases of the eye. Disease can be monophasic, recurrent or chronic.

 

The researchers also add that a gene mutation – APOEe4 – which appears to makes some of the population more susceptible to Alzheimer’s disease, could also increase these people’s susceptibility to infectious diseases.

 As a counter view, Professor John Hardy, Teacher of Neuroscience, UCL, told the website Journal Focus he was doubtful about the claims: “This is a minority sight in Alzheimer research study. There had actually been no convincing evidence of infections triggering Alzheimer disease. We require constantly to maintain an open mind however this editorial does not show exactly what many scientists think of Alzheimer disease.”

However, another of the researchers, Resia Pretorius of the University of Pretoria, told Bioscience Technology: “The microbial presence in blood may also play a fundamental role as causative agent of systemic inflammation, which is a characteristic of Alzheimer’s disease. Furthermore, there is ample evidence that this can cause neuroinflammation and amyloid-β plaque formation.”

The possibility of transfer has been reported to the journal Nature. The paper is titled “Evidence for human transmission of amyloid-β pathology and cerebral amyloid angiopathy.”

The report explains that during the period from 1958 to 1985, 30,000 people worldwide — mainly children — were administered injections of human growth hormone. This was designed to treat short stature. The hormone was extracted from thousands of human pituitary glands, with the source material being recently deceased people.

It now appears, The Economist summarizes, that some of these hormonal extracts contained prions. Around one in 16 of the children developed the brain disorder Creutzfeldt-Jakob disease (CJD). The concern with CJD centered on prions.

Read more: http://www.digitaljournal.com/science/alzheimer-s-and-parkinson-s-diseases-may-be-transmissible/article/444338#ixzz43Y

Chain reaction

Evidence emerges that Alzheimer’s disease, and other neurodegenerative disorders such as Parkinson’s, may be transmissible

 

KAREN WEINTRAUB

Reporting from the frontiers of health and medicine

A rare disease killed her mother. Can this scientist save herself?

http://www.statnews.com/2016/01/20/prion-disease-genes/

CAMBRIDGE, Mass. — Five years ago, after watching her 51-year-old mother descend quickly into dementia, disability, and then death, Sonia Vallabh learned she was destined for the same fate. They both shared an extremely rare genetic mutation that leads a protein in the brain to turn toxic.

Vallabh, then a recent Harvard Law School graduate working as a consultant, decided to quit her job to spend time learning more about the mutation and nascent efforts to understand and treat it.

Now, she and her husband, Eric Minikel, a former transportation planner, are first authors on a paper about so-called prion diseases. Published Wednesday in Science Translational Medicine, the paper found that not all prion gene mutations are an early death sentence — though Vallabh’s variation is.

The husband-and-wife team, now both PhD students working in the same lab at the Broad Institute, also found that people can survive with only one copy of the prion gene, suggesting that a treatment to block the mutated version can be delivered safely.

Prion diseases were made famous by “mad cow disease,” outbreaks of which have led to mass killings of cattle. Eating sick cows can cause the fatal neurodegenerative illness known as Creutzfeldt-Jakob disease. But there are genetic versions of prion diseases that account for about 15 percent of cases. They come from mutations to the prion protein gene PRNP, which causes a protein in the brain to fold the wrong way, forming toxic clumps. Once these proteins get a foothold in the brain, they can cause extremely rapid damage.

Vallabh’s mother, who seemed completely normal at Christmastime in 2009, showed the first symptoms of disease in January 2010 and was demented and unable to speak clearly by March. She last recognized her daughter in May, Vallabh said, and died two days before Christmas that year, shortly after doctors finally identified the cause of her bizarre symptoms.

Vallabh, Minikel, and their coauthors compared a data set — painstakingly collected over decades — of gene sequences from 16,000 prion disease patients from all over the world, with two data sets of sequences from healthy people: more than 60,000 collected by the Broad-led Exome Aggregation Consortium and 530,000 from 23andMe, a consumer genetics company that invites clients to volunteer their gene sequences for research.

The size of the data sets allowed the researchers to draw conclusions even with a condition as rare as prion disease. Doctors had previously only known about 63 possible mutations in people with disease, so they had thought that all the mutations necessarily caused problems. But the researchers found 141 healthy people in the 23andMe dataset who had mutations to the PRNP gene — a rate far higher than the incidence of prion disease. That means some of the mutations must be harmless or at least not always cause disease, said J. Fah Sathirapongsasuti, a computational biologist at 23andMe and a study coauthor.

Out of 16 mutations for which there was evidence in the larger populations, they concluded that three were likely benign, three caused somewhat increased risk of disease, and four others, including Vallabh’s mutation, definitely do cause the fatal illness, they found.

They also discovered three older, healthy people who carried only one functional copy of the PRNP gene. That means that knocking out the mutated version of PRNP with gene therapy, or tamping down its activity with drugs, should be an effective way to eliminate the risk of disease without causing life-threatening problems.

Their paper has already helped at least one person, according to Dr. Robert Green, a medical geneticist at Brigham and Women’s Hospital, who cowrote an opinion piece published alongside the new study.

One of Green’s patients, whose mother died of prion disease, had been told her mom’s mutation — which she didn’t inherit, but her sister did — was always fatal. After seeing the new study, Green was able to inform the sister that her mutation was most likely harmless.

 

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Alzheimer’s disease, snake venome, amyloid and transthyretin

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Significant points:

  • Alzheimer’s Disease is characterized by amyloid plaques
  • The plaques have amyloid beta and tau
  • Toxic proteins accumulate in AD
  • snake venome activates enzymes (Endothelin Converting Enzyme-1 and Neprilysin) that break down the plaques that are sufficient in non-AD brain
  • Aβ peptides derive from proteolytic processing of a large (695/770 amino acids) type 1 transmembrane glycoprotein known as amyloid beta precursor protein (APP)
  • a natural variant of Amyloid-β (Aβ) carrying the A2V substitution protects heterozygous carriers from AD by its ability to interact with wild-type Aβ, hindering conformational changes and assembly
  • aggregated Aβ species, particularly oligomeric assemblies, trigger a cascade of events that lead to hyperphosphorylation, misfolding and assembly of the tau protein with formation of neurofibrillary tangles
  • [Aβ1-6A2VTAT(D)] revealed strong anti-amyloidogenic effects in vitro and protected human neuroblastoma cells from Aβ toxicity
  • while both Aβ1-6A2V and Aβ1-6WT display a predominant coil configuration, Aβ1-6A2V shows a slightly higher propensity to form secondary structure motifs involving two to three residues
  • Aβ1-6A2VTAT(D) maintains the in vitro anti-amyloidogenic properties of Aβ1-6A2V(D)
  • Transthyretin (TTR) influences plasma Aβ by reducing its levels
  • Transthyretin (TTR) binds Aβ peptide, preventing its deposition and toxicity
  • TTR facilitated peptide internalization of Aβ1-42 uptake by primary hepatocytes
  • Brain permeability to TTR
  • TTR regulates LRP1 levels, suggesting that TTR uses this receptor to promote Aβ clearance

 

Snake venom may hold key to breaking down plaques that cause Alzheimer’s disease

March 2, 2016  http://medicalxpress.com/news/2016-03-snake-venom-key-plaques-alzheimer.html

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4750079/bin/srep20949-f2.jpg

http://www.ncbi.nlm.nih.gov/pmc/articles/instance/4750079/bin/srep20949-f2.jpg

Alzheimer’s disease, snake venome, amyloid and transthyretin

 

Snake venom may hold key to breaking down plaques that cause Alzheimer’s disease

http://img.medicalxpress.com/newman/csz/news/800/2016/snakevenomma.jpg

A toxic protein called amyloid beta is thought to play a key role in the onset of Alzheimer’s disease. In healthy people, amyloid beta is degraded by enzymes as it forms. However, in patients with the disease, these enzymes appear unable to adequately perform their actions, causing the toxic protein to accumulate into plaque deposits, which many researchers consider leads to dementia.

One of the Holy Grails of the pharmaceutical industry has been to find a drug that stimulates these enzymes in people, particularly those who are in the early stages of dementia, when amyloid plaques are just starting to accumulate.

Monash researchers have discovered what could well be this elusive drug candidate– a molecule in snake venom that appears to activate the enzymes involved in breaking down the amyloid plaques in the brain that are the hallmark of Alzheimer’s disease. Dr Sanjaya Kuruppu and Professor Ian Smith from Monash University’s Biomedicine Discovery Institute have just published their research in Nature Scientific Reports.

Dr Kuruppu has spent most of his research life studying snake venoms, looking for drug candidates.  When he began researching Alzheimer’s disease he says that “snake venom was an obvious place for me to start.”

He was looking for a molecule that would stimulate the enzymes to break down the amyloid plaques.  What he found, when screening various snake venoms, was in fact one molecule with the ability to enhance the activity of two plaque degrading enzymes. This molecule was extracted from a venom of a pit viper found in South and Central America. Dr Kuruppu and his team have developed synthetic versions of this molecule. Initial tests done in the laboratory using human cells have shown it to have the same effects as the native version found in the snake venom.

Dr Kuruppu is one of the four researchers in Australia to receive funding from the National Foundation for Medical Research and Innovation to conduct further testing of this newly-identified molecule.

Explore further: Alzheimer protein’s structure may explain its toxicity

More information: A. Ian Smith et al. N-terminal domain of Bothrops asper Myotoxin II Enhances the Activity of Endothelin Converting Enzyme-1 and Neprilysin, Scientific Reports (2016).
http://dx.doi.org:/10.1038/srep22413

 

N-terminal domain of Bothrops asper Myotoxin II Enhances the Activity of Endothelin Converting Enzyme-1 and Neprilysin

  1. Ian Smith, Niwanthi W. Rajapakse, Oded Kleifeld, Bruno Lomonte,…, Helena C. Parkington, James C. Whisstock & Sanjaya Kuruppu

Scientific Reports 6, Article number: 22413 (2016)    http://www.nature.com/articles/srep22413

 

Neprilysin (NEP) and endothelin converting enzyme-1 (ECE-1) are two enzymes that degrade amyloid beta in the brain. Currently there are no molecules to stimulate the activity of these enzymes. Here we report, the discovery and characterisation of a peptide referred to as K49-P1-20, from the venom of Bothrops asper which directly enhances the activity of both ECE-1 and NEP. This is evidenced by a 2- and 5-fold increase in the Vmax of ECE-1 and NEP respectively. The K49-P1-20 concentration required to achieve 50% of maximal stimulation (AC50) of ECE-1 and NEP was 1.92 ± 0.07 and 1.33 ± 0.12 μM respectively. Using BLITZ biolayer interferometry we have shown that K49-P1-20 interacts directly with each enzyme. Intrinsic fluorescence of the enzymes change in the presence of K49-P1-20 suggesting a change in conformation. ECE-1 mediated reduction in the level of endogenous soluble amyloid beta 42 in cerebrospinal fluid is significantly higher in the presence of K49-P1-20 (31 ± 4% of initial) compared with enzyme alone (11 ± 5% of initial; N = 8, P = 0.005, unpaired t-test). K49-P1-20 could be an excellent research tool to study mechanism(s) of enzyme stimulation, and a potential novel drug lead in the fight against Alzheimer’s disease.

Metalloproteases play a central role in regulating many physiological processes and consequently abnormal activity of these enzymes contribute to a wide range of disease pathologies. These include cardiovascular1 and neurodegenerative disease2 as well as many types of cancers1. Inhibitors of metalloproteases are widely used in research applications with some also approved for use in the clinic. However, molecules which stimulate the activity of these enzymes are rarely encountered, and as such our understanding of the mechanism(s) behind enzyme stimulation remains poor. Stimulators of enzyme activity can provide novel insights into enzyme biology and potentially open up avenues for the design of a novel class of drugs. For instance, ECE-1 and NEP are two metalloproteases that degrade amyloid beta (Aβ), the accumulation of which is a hallmark of Alzheimer’s disease.

Therefore it is of great interest to regulate the production of, and more importantly, the degradation of Aβ by stimulating the activity of these enzymes2. This in turn could reverse, prevent or at least halt the progression of Alzheimer’s disease.

Previous studies using animal models of Alzheimer’s disease have shown that increasing the expression of ECE3 and NEP4 through DNA based techniques can have beneficial effects. However, DNA based approaches can pose challenges for clinical translation. Molecules which can directly stimulate the activity of ECE-1 and NEP, or increase their expression are more attractive alternatives. Several studies have reported on the presence of molecules which increase the expression of or activity of NEP5,6,7. However, there are no reports on molecules which stimulate the activity of ECE-1. For example, polyphenols in green tea have been reported to increase the activity of NEP in cell culture models5, while the neuroprotective hormone humanin has been shown to increase the expression of NEP in a mouse model of Alzheimer’s disease6. In addition, Kynurenic acid elevates NEP expression as well as activity in human neuroblastoma cultures and mouse cortical neurones7. Therefore this study aimed to identify a molecule which stimulates the activity of ECE-1. Here we report on the discovery of K49-P1-20, a 20 amino acid peptide from the venom of B. asper which stimulates the activity of both ECE-1 and NEP. The effect of this peptide on other closely related enzymes was also examined.

Identification of K49-P1-20

We screened venom from species across different geographical regions for their effects on ECE-1 activity. The venom from B. asper was found to stimulate the activity of ECE-1 (624 ± 27% of control; Fig. 1a). Fractionation of venom confirmed that ECE-1 stimulation was mediated by the previously isolated B. aspermyotoxin II (Fig. 1a), a lysine 49 (K49) type phospholipase A2 found in this venom which induces myonecrosis upon envenoming8. Digestion of B. asper myotoxin II with ArgC proteinase indicated that the stimulation of ECE-1 activity was mediated by its N-terminal region (Fig. 1a). The synthetic peptide K49-P1-34 corresponding to the N-terminal region mimicked the stimulator effects of B. asper myotoxin II (Fig. 1a,b). No significant difference in the activation was observed between peptides K49-P1-20 and K49-P1-34 (Fig. 1a). However, the level of stimulation observed in the presence of K49-P9-34 and inverted sequence of K49-P1-20 was significantly less compared with native K49-P1-20 (Fig. 1a). Further digestion of peptide K49-P1-20 resulted in a reduction in its ability to stimulate ECE-1 activity (Fig. 1c) indicating the importance of residues 1-20 for maximal stimulation of ECE-1 activity. Peptide K49-P1-20 failed to inhibit direct twitches of the chick biventer cervicis nerve muscle preparation, confirming its lack of myotoxic effects (Fig. 1d), in agreement with the previous mapping of toxicity determinants of B. asper myotoxin II to its C-terminal region9.

Figure 1

Figure 1

 

Discovery of K49-P1-20 (a) Comparison of ECE-1 stimulating effects of venom, B. asper myotoxin II, peptides K49-P1-20, K49-P1-34, K49-P9-34 and inverted K49-P1-20 (10 ng/μL); (b) Schematic showing the amino acid sequence of B. asper myotoxin II (ArgC mediated cleavage sites are indicated by arrows). The underlined sections correspond to the sequence of synthetic peptides tested for their effects on ECE-1 activity; (c) trypsin mediated cleavage of K49-P1-20 produces peptides K49-P1-7 and K49-P8-20 (cleavage sites indicated by arrows, top panel); the effect of K49-P1-20, peptides K49-P1-7 and K49-P8-20 on ECE-1 activity (bottom panel); (d) a representative trace showing the effect of K49-P1-20 (25 μg/mL) on direct twitches of the chick biventer cervices muscle. The arrow indicates the point of addition of peptide. *Significantly different than ECE-1 + peptide K49-P1-20, P < 0.05, unpaired t-test, n = 48.

Alanine scan

Alanine substitution of Leu(2) and Ile(9) failed to enhance ECE-1 activity, indicating their importance for stimulating ECE-1 (Fig. 3). Alanine substitution of Leu(2), Phe(3), Glu(4), Leu(10), Glu(12), Thr(13), Lys(15), Lys(19) and Ser(20) failed to enhance NEP activity, indicating their importance for stimulating NEP (Fig. 3).

Figure 3: Alanine scan.

A library of K49-P1-20 analogs were synthesised where each subsequent residue was replaced by an Ala. These analogs were tested for their ability to stimulate ECE-1 and NEP activity. The K49-P1-20 analogs are shown in the middle, with the Ala substitutions indicated in red. Closed bar denotes enzyme alone and the native peptide is indicated in blue *significantly different compared to enzyme alone; P < 0.05; One-way ANOVA; n = 4.

K49-P1-20 and enzyme interaction and conformational changes

BLITZ Biolayer interferometry

N-terminal biotinylation of K49-P1-20 had no significant effect on its ability to stimulate ECE-1 activity (Fig. 4a). Interaction of ECE-1 and NEP with biotinylated K49-P1-20 immobilised on a streptavidin biosensor was indicated by an increase in response units (nm) over time (Fig. 4b). The interaction was rapidly reversible. There was only a minimal interaction between each of the enzymes and biotinylated version of inverted K49-P1-20.

 

Figure 4: Association between K49-P1-20 and enzymes.

Figure 4

Figure 4

(a) Effect of N-terminal biotinylation of K49-P1-20 on the activity of ECE-1. (b) Representative traces obtained using Biolayer interferometry showing the level of interaction between enzymes and the biotinylated version of native or inverted K49-P1-20; representative traces showing the effect of K49-P1-20 on the intrinsic fluorescence of (c) ECE-1 and (d) NEP. Fluorescence of K49-P1-20 alone, and the sum of fluorescence intensities of K49-P1-20 and enzyme is also indicated.

K49-P1-20 stimulates ECE-1 activity in cerebrospinal fluid

K49-P1-20 (1–30 ng/μL) stimulated the activity of rhECE-1 in cerebrospinal fluid obtained from a patient with Alzheimer’s disease, as evidenced by the enhanced cleavage of bradykinin based QFS (Fig. 7a). Addition of stimulated ECE-1 to cerebrospinal fluid obtained from patients with Alzheimer’s disease (N = 8) resulted in a significant decrease (31 ± 4%) in the levels of endogenous soluble Aβ42 over 4 h, compared with the addition of non-stimulated ECE-1 (11 ± 5%; P = 0.005, unpaired t-testFig. 7b). This decrease was blocked by the ECE-1 specific inhibitor CGS35066 (Fig. 7b).

Figure 7: K49-P1-20 stimulates ECE-1 activity in cerebrospinal fluid

Figure 7

(a) the effect of K49-P1-20 (1–30 ng/μL) on the activity of rhECE-1(0.04–ng/μL) added to cerebrospinal fluid obtained from a patient with Alzheimer’s disease at post mortem. Enzyme activity was measured using the bradykinin based QFS. * & α significantly different compared to ECE-1 alone or K49-P1-20 (1 ng/μL) respectively; P < 0.001; n = 5; one-way ANOVA. (b) The effect of ECE-1 alone (0.04 ng/μL); ECE-1 incubated with K49-P1-20 (300 ng/μL); or ECE-1+ K49-P1-20 + ECE-1 inhibitor CGS35066 (500 nM), on the levels of endogenous Aβ42 in cerebrospinal fluid taken from a patient with Alzheimer’s disease at post-mortem was determined using a commercially available ELISA kit. Significantly different compared to *ECE-1 alone P = 0.005; or **ECE-1 + K49-P1-20, P = 0.009; unpaired t-test, N = 8–11.

Discussion

ECE-1 and NEP are two closely related metalloproteases that play a key role in many physiological and pathophysiological processes2,15,16. A common substrate to both enzymes is Aβ which plays a key role in the pathogenesis of Alzheimer’s disease2,15,16,17,18. Previous studies have reported the discovery of molecules which increase NEP activity5,6,7. However, there are no reports on molecules that increase ECE-1 activity. Here we report on the discovery of a peptide named K49-P1-20 from the venom of B. asper which stimulates the activity of both ECE-1 and NEP. Interaction of K49-P1-20 with ECE-1 or NEP appears to induce a change in its conformation leading to an increase in activity. Unlike the molecules reported in previous studies which increase NEP expression and therefore cellular NEP activity5,6,7, K49-P1-20 appears to allosterically regulate the activity of ECE-1 and NEP.

Animal venoms have long been a source of lead compounds for future pharmaceuticals and research tools19,20. We therefore screened venoms of snakes found in different geographical regions to identify a molecule that modulates the activity of ECE-1, and found that the venom of B. asper stimulated ECE-1 activity. Initial fractionation of venom indicated that this effect was mediated by a toxin known as B. asper myotoxin II which induces myonecrosis following envenoming8. B. aspermyotoxin II belongs to a class of toxins known as Lysine 49 phospholipase A2 myotoxins21. Asp to Lys substitution at position 49 is a key structural feature of these toxins and their toxic effects are independent of the phospholipase A2 activity. Digestion of this toxin with ArgC proteinase indicated that stimulation of ECE-1 activity was mediated by its N-terminal domain. The use of synthetic peptides of varying length corresponding to this region confirmed that these effects were in fact mediated by its first 20 amino acids. Inverted sequence of K49-P1-20 failed to induce an increase in ECE-1 activity (136 ± 12 as % of ECE-1 alone; n = 3-4), indicating that the specific sequence of K49-P1-20 is critical for the observed effects. Further shortening of this peptide resulted in a loss of ECE-1 stimulating effects. K49-P1-20 therefore appears to possess the shortest optimum sequence required for ECE-1 stimulation and was used in all downstream studies. Previous studies have shown that myotoxic effects of B. asper myotoxin II are mediated by is C-terminal domain9. In agreement with this result, K49-P1-20 showed no myotoxicity in chick biventer cervicis muscle.

Compared with enzyme alone, K49-P1-20 also significantly enhanced the activity (expressed as % of control) of closely related enzyme NEP (1606 ± 29), and two other metalloproteases ACE-2 (145 ± 8) and IDE (292 ± 38). The level of ACE-2 and IDE stimulation was however significantly less compared with NEP, therefore indicating degree of specificity towards ECE-1 and NEP. All further studies therefore focused on the effect of K49-P1-20 on ECE-1 and NEP activity. K49-P1-20 increased the activity of ECE-1 and NEP in a concentration dependant manner. The increase in activity of both enzymes become evident at a K49-P1-20 concentration of 0.23 μM, or a peptide: enzyme molar ratio of 1:368. The high level of ECE-1 and NEP stimulation observed in response to K49-P1-20 is most likely the result of a common binding region for K49-P1-20 within these enzymes. ECE-1 and NEP in deed share 40% sequence homology22. However the potential sites of interaction between the enzymes and K49-P1-20 are best identified through structural biology approaches that take into account the secondary and tertiary structure of the enzymes.

Physical interaction between the activating molecule and enzyme is a common characteristic in the mechanisms of enzyme activation23. We used biolayer interferometry to probe possible physical interaction between K49-P1-20 and ECE-1 or NEP. N-terminal biotinylation of K49-P1-20 had no significant impact on its ability to stimulate ECE-1 activity, thus facilitating its use as a tool in research applications. Biotinylated K49-P1-20 immobilised on a streptavidin biosensor interacted directly with both ECE-1 and NEP as evidenced by the increase in response units over time. This interaction however was not observed with the biotinylated version of inverted K49-P1-20.

It is logical to assume that a conformational change that occurs following interaction with K49-P1-20 mediates the increase in enzyme activity. We investigated this by examining the effect of K49-P1-20 on the intrinsic fluorescence of ECE-1 and NEP. Fluorescence spectra of each enzyme in the presence of K49-P1-20 were distinct from that of enzyme alone. In addition, the sum of individual spectra for K49-P1-20 and ECE-1 or NEP failed to overlap with the spectra obtained by incubating K49-P1-20 with enzymes. This suggests that spectral changes that occur in the presence of K49-P1-20 is the likely result of a change in conformation of the enzymes, which in turn is a possible consequence of a direct interaction with K49-P1-20.

 

Tackling amyloidogenesis in Alzheimer’s disease with A2V variants of Amyloid-β

Giuseppe Di Fede, Marcella Catania, Emanuela Maderna, Michela Morbin,…,,Fabio Moda, Matteo Salvalaglio, Mario Salmona  & Fabrizio Tagliavini

Scientific Reports 6, Article number: 20949 (2016)  http://dx.doi.org:/10.1038/srep20949

 

We developed a novel therapeutic strategy for Alzheimer’s disease (AD) exploiting the properties of a natural variant of Amyloid-β (Aβ) carrying the A2V substitution, which protects heterozygous carriers from AD by its ability to interact with wild-type Aβ, hindering conformational changes and assembly thereof. As prototypic compound we designed a six-mer mutated peptide (Aβ1-6A2V), linked to the HIV-related TAT protein, which is widely used for brain delivery and cell membrane penetration of drugs. The resulting molecule [Aβ1-6A2VTAT(D)] revealed strong anti-amyloidogenic effects in vitro and protected human neuroblastoma cells from Aβ toxicity. Preclinical studies in AD mouse models showed that short-term treatment with Aβ1-6A2VTAT(D) inhibits Aβ aggregation and cerebral amyloid deposition, but a long treatment schedule unexpectedly increases amyloid burden, although preventing cognitive deterioration. Our data support the view that the AβA2V-based strategy can be successfully used for the development of treatments for AD, as suggested by the natural protection against the disease in human A2V heterozygous carriers. The undesirable outcome of the prolonged treatment with Aβ1-6A2VTAT(D) was likely due to the TAT intrinsic attitude to increase Aβ production, avidly bind amyloid and boost its seeding activity, warning against the use of the TAT carrier in the design of AD therapeutics.

Alzheimer’s disease (AD) is the most common form of dementia in the elderly. Its clinical course is slow but irreversible since no disease-modifying treatments are currently available. As a result, this illness has a huge socio-sanitary impact and designing of effective therapies is considered a public health priority.

A central pathological feature of AD is the accumulation of misfolded Amyloid-beta (Aβ) peptides in the form of oligomers and amyloid fibrils in the brain1,2,3. It has been advanced that aggregated Aβ species, particularly oligomeric assemblies, trigger a cascade of events that lead to hyperphosphorylation, misfolding and assembly of the tau protein with formation of neurofibrillary tangles and disruption of the neuronal cytoskeleton, widespread synaptic loss and neurodegeneration. According to this view, altered Aβ species are the primary cause of AD and the primary target for therapeutic intervention3,4.

Aβ peptides derive from proteolytic processing of a large (695/770 amino acids) type 1 transmembrane glycoprotein known as amyloid beta precursor protein (APP). APP is cleaved at the N-terminus of the Aβ domain by β-secretase, forming a large, soluble ectodomain (sAPPβ) and a 99-residue, membrane-retained C-terminal fragment (C99). Subsequently, γ-secretase cleaves C99 to release Aβ with different carboxyl termini, including Aβ40, Aβ42 and other minor species5. APP may undergo an alternative, non-amyloidogenic processing where the protein is cleaved within the Aβ domain by α-secretase, forming a soluble ectodomain (sAPPα) and an 83-residue C-terminal fragment (C83)5,6.

We identified a novel mutation in the APP gene resulting in A-to-V substitution at codon 673, corresponding to position 2 in the Aβ sequence7. Studies on biological samples from an A673V homozygous carrier, and cellular and C. elegans models indicated that this mutation shifts APP processing towards the amyloidogenic pathway with increased production of amyloidogenic peptides. Furthermore, the A2V substitution in the Aβ sequence (AβA2V) increases the propensity of the full-length peptides (i.e., Aβ1-40 and Aβ1-42) to adopt a β-sheet structure, boosts the formation of oligomers both in vitroand in vivo and enhances their neurotoxicity8,9,10. Following the observation that humans carrying the mutation in the heterozygous state do not develop AD, we carried out in vitro studies with synthetic peptides that revealed the extraordinary ability of AβA2V to interact with wild-type Aβ (AβWT), interfering with its nucleation or nucleation-dependent polymerization7. This provides grounds for developing a disease-modifying therapy for AD based on modified AβA2V peptides retaining the key functional properties of parental full-length AβA2V.

Following this approach, we generated a mutated six-mer peptide (Aβ1-6A2V), constructed entirely by D-amino acids [Aβ1-6A2V(D)] to increase its stability in vivo, whose interaction with full-length AβWT hinders oligomer production and prevents amyloid fibril formation8.

These results prompted us to develop a prototypic compound by linking Aβ1-6A2V(D) to an all-D form of TAT sequence [TAT(D)], a peptide derived from HIV that powerfully increases virus transmission to neighbour cells11, and is widely used for brain delivery of drugs12,13,14. Here we report that this compound [Aβ1-6A2VTAT(D)] has strong anti-amyloidogenic effects in vitro, leading to inhibition of oligomer, amyloid fibril formation and of Aβ-dependent neurotoxicity. Preclinical studies showed that a short-term treatment with this peptide in an AD mouse model prevents Aβ aggregation and amyloid deposition in the brain but longer treatment unexpectedly increases amyloid burden, most likely due to the TAT intrinsic attitude to enhance Aβ production and to avidly bind amyloid and boost its seeding activity, warning against the use of this carrier in therapeutic approaches for AD.

In silico molecular modeling of AβA2V peptide variants

To predict the structural basis of the anti-amyloidogenic effect of Aβ1-6A2V(D), a comparative conformation analysis of WT and mutated Aβ1-6 was carried out with all-atom classical MD simulations in explicit solvent. Both Aβ1-6WT and Aβ1-6A2V are intrinsically disordered peptides characterized by high flexibility. Nevertheless, the substitution of Ala2 with a Val residue induces significant changes in the appearance of the peptide in solution, resulting in an increase of the apolar character of the solvent accessible surface (SAS) (Fig. 1A) and in a modification of the gyration radius distribution in the Aβ1-6A2V. Figure 1B shows that the probability distribution of the gyration radius is characterized by a global shift to smaller values and by the appearance of a shoulder in the distribution corresponding to gyration radii of 0.5 nm.

Figure 1: Analysis of 1.5 μs explicit solvent MD simulations of the Aβ1-6WT and Aβ1-6A2V peptides.

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(A) Apolar character of the peptide SAS represented as the ratio between SASapolar and the total SAS. (B) Gyration radius distribution. (C) Analysis of secondary structure propensity. “Structure” indicates residues possessing a defined secondary structure, in this case structure indicates residues in a “turn” configuration. “Coil” indicates residues that do not display a defined secondary structure. Analysis of the secondary structure was carried out with DSSP. (D) Typical compact “turn” and elongated “coil” configurations reported for the Aβ1-6A2V and Aβ1-6WT, respectively. (E) Analysis of the most populated structural clusters. Representative structures of the six most probable clusters were reported. The coil configuration has been highlighted in green, the turn in red and a partly folded turn in orange.

An analysis of the secondary structure content displayed by the peptides (Fig. 1C) shows that, while both Aβ1-6A2Vand Aβ1-6WT display a predominant coil configuration, Aβ1-6A2V shows a slightly higher propensity to form secondary structure motifs involving two to three residues. Aβ1-6A2V in fact displays a propensity to form a turn involving the Glu3, Phe4 and Arg5 residues (Fig. 1D). The most populated structural clusters15 (Fig. 1E), in Aβ1-6WT are characterized by an elongated coil structure accounting for 52.6% of the configurations, while the compact “turn” state is only the third most probable cluster, with a population of around 9%. Conversely, in the Aβ1-6A2V, while the most populated structure is still an elongated coil (32%), the “turn” configuration is the second most populated structural cluster (31%).

Both Aβ1-6WT and Aβ1-6A2V under physiological conditions are characterized by intramolecular salt bridges such as those between Asp1 and Arg5 or Glu3-Arg5. In the extended coil configuration (Fig. 1E), salt bridges can be dynamically formed and dissociated without requiring a specific rearrangement of the peptide backbone. However, in the turn configuration salt bridges are typically dissociated; the interaction of the apolar Val2 sidechain with the Arg5 sidechain stabilizes such a dissociated state. The additional sterical hindrance to the rearrangement induced by the Val2 sidechain also contributes to the stabilization of the turn configuration of the A2V peptide.

The propensity of the A2V mutant to adopt a Glu3-Arg5 turn configuration characterized by a significant lifetime can be interpreted as the probable source of the heterotypic interaction of the Aβ1-6A2V with full-length Aβ, which results in hindering its assembly.

Aβ1-6A2V retains the in vitro anti-amyloidogenic features of the parental full-length peptide

 

We previously showed that Aβ1-6A2V(D) destabilizes the secondary structure of Aβ1-42WT8 and is even more effective than the WT peptide [Aβ1-6WT(D)] and the A2V-mutated L-isomer [Aβ1-6A2V(L)] at preventing the aggregation of full-length AβWT8.

Treatment of SH-SY5Y cells with Aβ1-6WT(D) or Aβ1-6A2V(D) showed that neither is toxic for living cells even at high concentrations (20 μM) (Fig. 2A,B) and that both peptides are able to reduce the toxicity induced by Aβ1-42WT (Fig. 2C,D). However, Aβ1-6A2V(D) showed a stronger effect in counteracting the reduction of cell viability caused by Aβ1-42WT (Fig. 2D), suggesting that the A-to-V substitution actually amplifies the protective effects of the six-mer peptide.

Figure 2: Analysis of the effects of Aβ1-6WT(D), Aβ1-6A2V(D) and Aβ1-6A2VTAT(D) on neurotoxicity in cell models.

SH-SY5Y cells were differentiated with 10 μM retinoic acid. After 6 days the proper peptide was added to culture medium and cell viability was assessed after 24 h by MTT test. (A,B) Neither Aβ1-6WT(D) nor Aβ1-6A2V(D) are significantly toxic when added to culture medium of differentiated SH-SY5Y cells. Conversely, Aβ1-42WT reduces cell viability by 35%. * Significance vs non-treated cells. (C,D) Both Aβ1-6WT(D) and Aβ1-6A2V(D) are able to counteract the toxic effect of Aβ1-42WT. Aβ1-6A2V(D) showed a stronger effect than Aβ1-6WT(D). (E) Aβ1-6A2VTAT(D) is not toxic when added to culture medium at concentrations ranging between 1 and 5 μM, while it reduces cell viability at higher concentrations. * Significance vs non-treated cells. (F) Aβ1-6A2VTAT(D) showed a dose-dependent effect in reducing Aβ1-42wt toxicity. Comparison of cell viability was performed by Student t-test.

Aβ1-6A2VTAT(D) maintains the in vitro anti-amyloidogenic properties of Aβ1-6A2V(D)

Aβ1-6A2V(D) alone does not efficiently cross either the blood brain barrier (BBB) or cell membranes (data not shown). This is an important feature that would deeply limit its use as an in vivo anti-amyloidogenic drug. So, we linked this peptide to the all-D TAT sequence to improve the translocation of Aβ1-6A2V(D) across the BBB and cell membranes, minimize the degradation of the peptide and reduce the immune response elicited by the molecule. The resulting compound [Aβ1-6A2VTAT(D)] destabilizes the secondary structure of Aβ1-42WT. Indeed, CD spectroscopy studies showed that Aβ1-6A2VTAT(D) inhibits the acquisition of β-sheet conformation by Aβ1-42WT (data not shown), thus affecting the folding of the full-length peptide.

We tested the ability of Aβ1-6A2VTAT(D) to inhibit the fibrillogenic properties of the full-length Aβ in vitro and found that the compound hindered Aβ1-42WT aggregation (Fig. 3). Polarized light and electron microscopy studies on aggregates of Aβ1-42WT formed after 20 days incubation with or without Aβ1-6A2VTAT(D) revealed that the mutated peptide hinders the formation of amyloid structures (Fig. 3B) and reduces the amount of fibrils generated by the full-length peptide (Fig. 3D). Moreover, AFM analysis (Fig. 3E,H) showed that Aβ1-6A2VTAT(D) actually interferes with the oligomerization process of Aβ1-42WT. Indeed, monomeric Aβ1-42WT, incubated alone at a final concentration of 100 μM, formed a family of small oligomers of different size within a range of 6-20 nm in diameter (~ 70%) (Fig. 3E,G). Conversely, the co-incubation with Aβ1-6A2VTAT(D) resulted in the formation of very small globular structures with a range of 5-8 nm in diameter and height of 200-400 pm (~ 70%), large and thin structures, apparently very rich in water (width: 500–700 nm; height: 200–500 pm). Notably, only rare oligomeric structures were detected (Fig. 3F,H).

Figure 3: Inhibition of aggregation of Aβ1-42WT by Aβ1-6A2VTAT(D).

Figure 3

Polarized-light (A,B), electron microscopy (C,D) and atomic force microscopy (AFM) (E–H) studies showing the inhibitory effects of Aβ1-6A2VTAT(D) on amyloid formation, fibril production and oligomerization by Aβ1-42WT. In polarized-light and EM studies, both peptides were used at 0.125 mM, molar ratio = 1:1 or 1:4 respectively, with 20 days incubation. From 5–20 days, 1:1 co-incubation of the two peptides (B,D) displayed a lower amyloid fibril content respect to Aβ1-42WT alone (A,C), showing protofibrils, short fibrils and disaggregated granular material.E,F: Representative Tapping mode of AFM images as determined by amplitude error data of Aβ1-42WT oligomers. Aβ1-42WT peptide 100 μM in phosphate buffer 50 mM, pH 7.4 was incubated at 4 °C for 24 h alone (E) (Z range: -10/ + 10 mV) or in presence of Aβ1-6A2VTAT(D) (F) (Z range: -10/ + 25 mV). The molar ratio of Aβ1-42WT to Aβ1-6A2VTAT(D) was 1:4. Scale bar: 1 μm, inset: 200 nm. (G,H): height plot profiles obtained along different lines traced on the topographic AFM images. Overall, these effects were already evident in the 1:1 mixture of the two peptides (data not shown), suggesting that the inhibition of Aβ1-42WT aggregation by Aβ1-6A2VTAT(D) is a dose-dependent effect.

These effects were observed by incubating Aβ1-42WT and Aβ1-6A2VTAT(D) at a 1:4 molar ratio, but they were also evident at equimolar concentrations of the two peptides.

Moreover, treatment of differentiated SH-SY5Y cells with Aβ1-6A2VTAT(D) showed that the peptide is not toxic when administered at concentrations ranging between 1 and 5 μM (Fig. 2E). When co-incubated with Aβ1-42WT, Aβ1-6A2VTAT(D) displayed a significant dose-dependent reduction of the toxicity induced by full-length Aβ (Fig. 2F).

All these findings indicated that the designed Aβ1-6A2VTAT(D) peptide is particularly efficient at inhibiting Aβ polymerization and toxicity in vitro, and identified it as our lead compound for the subsequent in vivo studies.

During the last few decades, huge efforts have been made to develop disease-modifying therapies for Alzheimer, but the results of these attempts have been frustrating. The anticipated increase of AD patients in the next few decades makes the development of efficient treatments an urgent issue16. In order to prevent the disease and radically change its irreversible course, a long series of experimental strategies against the main molecular actors of the disease (Aβ and tau)17 or novel therapeutic targets18 have been designed based on purely theoretical grounds19 as well as on evidence mainly deriving from preclinical observations in AD animal models20. However, few strategies proved suitable for application in human clinical trials, and none proved to be really effective21.Our approach differs from previous strategies – mainly those involving modified Aβ peptides that have been found to inhibit amyloidogenesis19,22 – since it is based on a natural genetic variant of amyloid-β (AβA2V) that occurs in humans and prevents the development of the disease when present in the heterozygous state7.

In this context, we carried out in vitro and in vivo studies that revealed the extraordinary ability of AβA2V to interact with AβWT, interfering with its aggregation8. These findings were a proof of concept of the validity of therapeutic strategies based on the use of AβA2V variant, and prompted us to develop a new disease-modifying treatment for AD by designing a six-mer mutated D-isomer peptide [Aβ1-6A2V(D)] linked to the short amino acid sequence derived from the HIV TAT peptide, widely used for brain delivery, to make the translocation of Aβ1-6A2V(D) across the BBB feasible.

The use of TAT as a carrier for brain delivery of drugs has been employed in several experimental approaches for the treatment of AD-like pathology in mouse models12,13. Recently, intraperitoneal administration of a TAT-BDNF peptide complex for 1 month was shown to improve the cognitive functions in AD rodent models23.

A previous study showed that, following its peripheral injection, a fluorescein-labelled version of TAT is able to cross the BBB, bind amyloid plaques and activate microglia in the cerebral cortex of APPswe/PS1DE9 transgenic mice24. TAT was then conjugated with a peptide inhibitor (RI-OR2, Ac-rGffvlkGr-NH2) consisting of a retro-inverted version of Aβ16–20 sequence25 that was found to block the formation of Aβ aggregates in vitro and to inhibit the toxicity of Aβ on cultured cells25. Daily i.p. injection of RI-OR2-TAT for 21 days into 10-month-old APPswe/PS1DE9 mice resulted in a reduction in Aβ oligomer levels and amyloid-β burden in cerebral cortex24.

We followed a similar strategy and initially demonstrated that Aβ1-6A2V(D), with or without the TAT sequence, retains in vitro the anti-amyloidogenic properties of the parental full-length mutated Aβ, since it is effective at hindering in vitro the production of oligomers and fibrils, the formation of amyloid and the toxicity induced by Aβ1-42WT peptide on SYSH-5Y cells.

Based on these results, we then decided to test in vivothe anti-amyloidogenic ability of Aβ1-6A2VTAT(D). The compound proved stable in serum after i.p. administration in mice, able to cross the BBB and associated with an immune response that was not found to cause any brain damage.

Short-term treatment with Aβ1-6A2VTAT(D) in the APPswe/PS1DE9 mouse model prevented cognitive deterioration, Aβ aggregation and amyloid deposition in brain. Unexpectedly, a longer treatment schedule, while retaining the results for cognitive impairment, attenuated the effects on Aβ production and increased amyloid burden, most likely due to the intrinsic amyloidogenic properties of TAT.

 

Indeed, we found that TAT(D), unlike Aβ1-6A2V(D), has a strong ability to bind amyloid deposits. This avidity for amyloid could boost the intrinsic seeding activity of amyloid plaques via a continuous and self-sustained recruitment of Aβ aggregates, leading to an exacerbation of the amyloidogenesis.

A similar effect of TAT was described in a study26reporting that HIV TAT promotes AD-like pathology in an AD mouse model co-expressing human APP bearing the Swedish mutation and TAT peptide (PSAPP/TAT mice). These mice indeed showed more Aβ deposition, neurodegeneration, neuronal apoptotic signalling, and phospho-tau production than PSAPP mice.

Moreover, TAT was found to increase Aβ levels by inhibiting neprilysin27 or enhancing β-secretase cleavage of APP, resulting in increased levels of the C99 APP fragment and 5.5-fold higher levels of Aβ4228. The same study reported that stereotaxic injection of a lentiviral TAT expression construct into the hippocampus of APP/presenilin-1 (PS1) transgenic mice resulted in increased TAT-mediated production of Aβ in vivo as well as an increase in the number and size of Aβ plaques. This is consistent with our findings, indicating a shift in APP processing towards the amyloidogenic processing in vivo at the end of the 5-month treatment with Aβ1-6A2VTAT(D) that was not observed in shorter treatment schedules with the same compound.

Therefore, these data suggest that the final outcome of our in vivo studies with Aβ1-6A2VTAT(D) is the result of side effects of the TAT carrier, whose amyloidogenic intrinsic activity neutralized the anti-amyloidogenic properties of the AβA2V variant. Nevertheless, we believe that the approach based on the use of AβA2V variant can be successfully used in treating AD, because of its potential ability to tackle the main pathogenic events involved in the disease, as suggested by the natural protection against the disease which occurs in human heterozygous A673V carriers.

 

Transthyretin participates in beta-amyloid transport from the brain to the liver- involvement of the low-density lipoprotein receptor-related protein 1?

Mobina Alemi, Cristiana Gaiteiro, Carlos Alexandre Ribeiro, Luís Miguel Santos,João Rodrigues Gomes,…, Ignacio Romero, Maria João Saraiva  & Isabel Cardoso

Scientific Reports 6, Article number: 20164 (2016)   http://dx.doi.org:/10.1038/srep20164

Transthyretin (TTR) binds Aβ peptide, preventing its deposition and toxicity. TTR is decreased in Alzheimer’s disease (AD) patients. Additionally, AD transgenic mice with only one copy of the TTR gene show increased brain and plasma Aβ levels when compared to AD mice with both copies of the gene, suggesting TTR involvement in brain Aβ efflux and/or peripheral clearance. Here we showed that TTR promotes Aβ internalization and efflux in a human cerebral microvascular endothelial cell line, hCMEC/D3. TTR also stimulated brain-to-blood but not blood-to-brain Aβ permeability in hCMEC/D3, suggesting that TTR interacts directly with Aβ at the blood-brain-barrier. We also observed that TTR crosses the monolayer of cells only in the brain-to-blood direction, as confirmed by in vivo studies, suggesting that TTR can transport Aβ from, but not into the brain. Furthermore, TTR increased Aβ internalization by SAHep cells and by primary hepatocytes from TTR+/+ mice when compared to TTR−/− animals. We propose that TTR-mediated Aβ clearance is through LRP1, as lower receptor expression was found in brains and livers of TTR−/− mice and in cells incubated without TTR. Our results suggest that TTR acts as a carrier of Aβ at the blood-brain-barrier and liver, using LRP1.

Alzheimer’s disease (AD), described for the first time by Alois Alzheimer in 1906, is characterized by progressive loss of cognitive functions ultimately leading to death1. Pathologically, the disease is characterized by the presence of extraneuronal amyloid plaques consisting of aggregates of amyloid-beta (Aβ) peptide, and neurofibrillary tangles (NFTs) which are intracellular aggregates of abnormally hyperphosphorylated tau protein2. Aβ peptide is generated upon sequential cleavage of the amyloid precursor protein (APP), by beta- and gamma-secretases, and it is believed that an imbalance between Aβ production and clearance results in its accumulation in the brain.

Clearance of Aβ from the brain occurs via active transport at the blood-brain-barrier (BBB) and blood cerebrospinal fluid (CSF) barrier (BCSFB), in addition to the peptidolytic removal of the peptide by several enzymes. The receptors for Aβ at the BBB bind Aβ directly, or bind to one of its carrier proteins, and transport it across the endothelial cell. The low-density lipoprotein receptor-related protein 1 (LRP1) and the receptor for advanced glycation end products (RAGE) are involved in receptor-mediated flux of Aβ across the BBB3. Both LRP1 and RAGE are multi-ligand cell surface receptors that, in addition to Aβ, mediate the clearance of a large number of proteins. While LRP1 appears to mediate the efflux of Aβ from the brain to the periphery, RAGE has been strongly implicated in Aβ influx back into the central nervous system (CNS). With increasing age, the expression of the Aβ efflux transporters is decreased and the Aβ influx transporter expression is increased at the BBB, adding to the amyloid burden in the brain.

 

Transthyretin (TTR), a 55 kDa homotetrameric protein involved in the transport of thyroid hormones and retinol, has been proposed as a protective protein in AD in the mid-nineties, when Schwarzman and colleagues described this protein as the major Aβ binding protein in CSF. These authors described that TTR was able to inhibit Aβ aggregation and toxicity, suggesting that when TTR fails to sequester Aβ, amyloid formation occurs4,5. Data showing that TTR is decreased in both CSF6 and plasma7,8 of AD patients, strengthen the idea of neuroprotection by TTR. Evidence coming from in vivostudies in AD transgenic mice established in different TTR genetic backgrounds9,10 also suggests that TTR prevents Aβ deposition and protects against neurodegeneration, although the exact mechanism is still unknown. Ribeiro and colleagues reported increased Aβ levels in both brain and plasma of AD mice with only one copy of the TTR gene, when compared to animals with two copies of the gene11, suggesting a role for TTR in Aβ clearance. Growing evidence also suggests a wider role for TTR in CNS neuroprotection, including in ischemia12, regeneration13 and memory14.

The presence of TTR in brain areas other than its site of synthesis and secretion – the choroid plexus (CP) and CSF, respectively–in situations of injury, such as ischemia, has been shown using a mouse model with compromised heat-shock response12. Authors showed that TTR was not being locally synthesized, but instead should derive from CSF TTR. However, other studies demonstrated TTR synthesis by cortical15 or hippocampal neurons both in vitro16, and in vivo17, and some hints on its regulation have already been advanced. Kerridge and colleagues showed that TTR is expressed in SH-SY5Y neuroblastoma cell line, and that it is up-regulated by the AICD fragment of amyloid precursor protein (APP), specifically derived from the APP695 isoform. Induced accumulation of functional AICD resulted in TTR up-regulation and Aβ decreased levels16. Wang and colleagues reported that TTR expression in SH-SY5Y cells, primary hippocampal neurons and hippocampus of APP23 mice is significantly enhanced by heat shock factor 1 (HSF1)17. In any case, TTR is available in the brain and might participate in brain Aβ efflux by promoting BBB permeability to the peptide. With regard to Aβ peripheral elimination, it is known that Aβ bound to ApoE/cholesterol can be incorporated in HDL to be further delivered at the liver for degradation18 and curiously, a fraction of TTR is transported in HDL19. Furthermore, the liver is the major site for TTR degradation and although its hepatic receptor has never been unequivocally identified, it has been reported that it is a RAP-sensitive receptor20. Thus, in this work we assessed the role of TTR in Aβ transport, both from the brain and to the liver.

TTR clearance in vivo

TTR ability to cross the BBB, in both directions, was studied in vivo using TTR −/− mice and injecting h rTTR. To assess the brain-to-blood permeability, immediately before the injection, mice were weighed and anesthetized with intraperitoneal injection of an anesthetic combination of ketamine and medetomidine (7.5 mg/Kg and 0.1 mg/Kg, respectively) and placed in a stereotaxic apparatus (Stoelting Co.). The cranium was exposed using an incision in the skin and one small hole was drilled through the cranium over the right lateral ventricle injection site to the following coordinates: mediolateral −1.0 mm, anterior-posterior −0.22 mm and dorsal-ventral −1.88 mm, from bregma. Then, 10 μg of h rTTR were injected into the brain using a 10 μL motorized syringe (Hamilton Co.) connected to a 30 gauge needle (RN Needle 6 pK, Hamilton Co.) at a rate of 0.75 μL/min (4 μL final volume). After injection, the microsyringe was left in place for 3 minutes to minimize any backflow, and then the incision was closed with sutures (Surgicryl), and the wound was cleaned with 70% ethanol. After surgery, the animals were kept warm, using a warming pad, and blood samples were collected by the tail vein after 20, 40 and 60 minutes, in a capillary tube (previously coated with EDTA). At the time of sacrifice (after 60 minutes), the mice were re-anesthetized with 75 mg/Kg ketamine and 1 mg/Kg medetomidine, and after total absence of reflexes in the paw and tail, mice were perfused through the injection of sterile PBS pH 7.4 via the inferior vena cava until the liver becomes blanched. Then, the brain was rapidly collected and frozen at −80 °C until use.

To assess the blood-to-brain permeability, 10 μg of h rTTR were injected in the tail vein, and blood samples were collected after 20, 40 and 60 minutes. At 60 minutes, and after perfusion as described above, CSF and brain were also collected.

To determine TTR levels, brains were weighted and homogenized in 750 μL of 50 mM TBS pH 7.4 containing protease inhibitor cocktail. After centrifugation for 20 minutes at 14000 rpm at 4 °C, supernatants were collected. TTR concentration in brain, CSF and plasmas was determined by ELISA.

Characterization of the hCMEC/D3 cell line

The hCMEC/D3 cell line represents a valid and powerfulin vitro tool as a BBB model, and presents a less expensive and more logistically feasible alternative to primary hBMEC cells24,25. Thus, our first step was the validation of the hCMEC/D3 model by characterizing this cell line regarding two critical features for our studies: BBB integrity and LRP1 expression.

In the context of endothelial cell tight junctions (TJ), hCMEC/D3 cells were tested for claudin-5 and occludin expression by immunofluorescence. As shown in Fig. 1, hCMEC/D3 cells are positive for TJ structural proteins, claudin-5 and occludin, showing the expected membrane localization (as previously described). These results indicate that the integrity, tightness and structure, as well as the paracellular contact between endothelial cells are guaranteed by these TJ proteins. Along with other TJ proteins expressed by hCMEC/D3, claudin-5 and occludin ensure, with high efficiency, the control of transport across the cells monolayer.

Figure 1: Immunofluorescence localization of TJs components Claudin-5 and Occludin, and of LRP1, in hCMEC/D3.

 

Figure 1

The expression of the efflux transport receptor LRP1 by the hCMEC/D3 cell line is a key factor when validating this model, both for BBB studies purposes and for Aβ transport research. Thus, we performed immunofluorescence analysis to verify if LRP1 exists in the hCMEC/D3 cells. Our results show that LRP1 is expressed in these cells ensuring the Aβ transport through the cells monolayer (Fig. 1).

Effect of TTR in Aβ1-42 internalization by hCMEC/D3

Aβ1-42 is transported across the BBB, as expected, and is internalized by hCMEC/D3 cells. We firstly investigated FAM-labelled Aβ1-42 (FAM-Aβ1-42, 500 ng/mL)) uptake by these cells in the absence and presence of human recombinant TTR (h rTTR) (7.5 μg/mL), and analysed the results by flow cytometry.

Cells were incubated with FAM-Aβ1-42 at 37 °C producing a rapid uptake of the peptide (Fig. 2A). After 5 minutes of incubation, 35–39% of the cells were fluorescent and after an additional 5 minutes (10 minutes incubation) a significant increase was already measured as over 57% of the cells were fluorescent, although differences between the presence and absence of TTR were not significant. However, after 15 minutes the presence of TTR significantly increased Aβ internalization resulting in about 73% fluorescent cells, in contrast to 61.7% incubated in the absence of TTR (Fig. 2A). Finally after 30 minutes of incubation, and although the difference between internalization levels at 15 and 30 minutes was not statistically significant, FAM-Aβ1-42 internalization was significantly higher in the presence of TTR.

Figure 2: Interaction of FAM-Aβ1-42 with hCMEC/D3 cells in the presence and absence of TTR assessed by flow cytometry:

Figure 2

(A) Internalization levels of FAM-Aβ1-42 by hCMEC/D3 cells in the presence of h rTTR (white columns) was significantly higher than in the absence of the protein (black columns) after 15 and 30 minutes of incubations. (B) Efflux of FAM-Aβ1-42 from hCMEC/D3 measured after 10 minutes of incubation with the peptide was significantly increased at 20 minutes post-replacement with fresh FAM-Aβ1-42-free media, in the presence of h rTTR. N = 3 for each condition and data are expressed as mean±SEM.

Next to investigate the fate of internalized Aβ, we performed an efflux assay. For that, hCMEC/D3 cells were firstly incubated with FAM-Aβ1-42 for 10 minutes, in the absence or presence of h rTTR and then the media were replaced with fresh Aβ-free media. Cells were further incubated at 37 °C and levels of FAM-Aβ1-42 inside cells were measured by flow cytometry, after 10 and 20 minutes. Figure 2B depicts the results showing that in the presence of TTR, FAM-Aβ1-42 effluxes significantly faster than in the absence of this protein, after 20 minutes (45.5% and 67.6% fluorescent cells, respectively).

Effect of TTR in hCMEC/D3 brain-to-blood permeability to Aβ1-42 peptide

In order to investigate the effect of TTR in Aβ1-42 transport across a monolayer of cells, acting as a model of the BBB as previously described, Aβ1-42 transport experiments were performed in hCMEC/D3 cultured in transwells inserts, as shown in Fig. 3A. Cells were grown for 10 days until reaching maximal confluence and allowing TJ formation. Thus, at this point, the cell monolayer should show restricted paracellular permeability, and its confirmation was done using FITC-labelled dextran as a low molecular weight paracellular diffusion marker. In this approach, FITC-labelled dextran 0.25 mg/mL was added to the apical chamber, and then incubated for 1 hour. Wells in which FITC-labelled dextran exceeded 125 ng/mL on the basolateral chamber were considered to have the monolayer disrupted and thus were excluded from the experiment.

Figure 3: Brain-to-blood permeability of hCMEC/D3 cells to Aβ1-42:

Figure 3

(A) Schematic representation of the transwell system used showing the brain and blood sides; Aβ1-42 peptide was always added to the brain side, whereas TTR was added either to the brain or to the blood sides. (B) Brain-to-blood permeability was increased in the presence of h rTTR although without reaching significant differences. However, in the presence of (C) hTTR present in sera, brain-to-blood permeability of hCMEC/D3 cells to Aβ1-42 was significantly increased after 3 hours up to 48 hrs. As a control, Aβ peptide was also added to non-seeded filters to show free passage of the peptide when compared to cell-seeded ones. N = 3 for each condition and data are expressed as mean±SEM. To mimic the absence of TTR, we used TTR-depleted human sera obtained after affinity chromatography, and further analysed by western blot (D) lanes 1- human sera; 2- protein G sepharose beads/anti-human prealbumin antibody; 3-human sera TTR-depleted; 4-Eluted TTR; 5-r hTTR.

We added h rTTR either to the brain or to the blood side, whereas Aβ1-42 was always added to the brain side. Results are displayed in Fig. 3B and show increased permeability of the hCMEC/D3 monolayer to Aβ1-42, when h rTTR is in the brain side, as compared to the levels of Aβ1-42 passage when h rTTR is in the blood side, although the differences were not statistically significant.

To further evaluate the effect of TTR in Aβ1-42 transport across the BBB and in order to obtain a more complex environment in hCMEC/D3 model, we performed the same transwell experiments but using human sera as source of hTTR (TTR concentration 7.5 μg/ml). To mimic the absence of TTR, we used human sera after TTR depletion by affinity chromatography (Fig. 3D). Again, hTTR present in the brain side promoted significant Aβ1-42 transport across the hCMEC/D3, as compared to the situation where hTTR was in the blood side (Fig. 3C). This suggests that TTR participates in Aβ1-42 efflux from the brain through a mechanism that implies TTR/Aβ interaction at the BBB or in its vicinity.

Brain permeability to TTR

Given our evidence in TTR-assisted Aβ transport and to clarify if TTR might be co-transported during such process, we assessed TTR internalization by hCMEC/D3 cells, and as shown in Fig. 4A, TTR was uptaken by these cells.

Figure 4: Permeability of hCMEC/D3 cells to TTR:

Figure 4

(A) hCMEC/D3 cells internalize TTR, as assessed by fluorescence microscopy. (B) hCMEC/D3 cells are permeable to TTR in the brain-to-blood direction but not in the blood-to-brain direction. N = 3 for each condition and data are expressed as mean±SEM.

We next investigated if TTR could cross the hCMEC/D3 monolayer and to assess this, hTTR was added either to the apical or basolateral compartment of the transwells. TTR was then quantified in the media of both chambers and analysed as % TTR that passed to the opposite side. As shown in Fig. 4B, TTR crosses the monolayer in the brain-to-blood direction but not in the blood-to brain direction. This suggests TTR is using a receptor with main expression in the basolateral membrane of the hCMEC/D3 cells.

To confirm these results, we also evaluated TTR clearance in vivo, using TTR−/− mice injected with h rTTR, either intracranially (IC) in the right lateral ventricle or intravenously (IV) in the tail vein. As displayed in Table 1, TTR injected in the brain rapidly reached the periphery as TTR was easily detected in blood, whereas mice injected IV showed negligible levels of the protein in the CSF and brain. Thus, this data corroborates the results obtained in the transwell experiments. This also suggests that TTR can favour Aβ brain efflux but cannot favour its influx, contributing to neuroprotection in AD.

Effect of TTR in Aβ1-42 and Aβ1-40 in AD transgenic mice

Previous work using an AD transgenic model (APPswe/PS1A246E) with different TTR genetic backgrounds (AD/TTR) has demonstrated that Aβ1-42 plasma levels are increased in 7-month old TTR+/− female mice, when compared to TTR+/+ animals11, suggesting a role for TTR in Aβ peripheral clearance.

In this work, to obtain a better knowledge on the effect of TTR in plasma Aβ peptide levels, we extended the study by evaluating not only Aβ1-42 but also Aβ1-40 levels in 3-months old AD/TTR+/+, AD/TTR+/− and AD/TTR−/− female mice. Results are depicted in Fig. 5 and show a negative correlation between TTR and both Aβ1-42 and Aβ1-40. Differences between AD/TTR+/+ and AD/TTR−/− mice were found to be statistical significant for both Aβ peptides. In addition, for Aβ1-42 statistical significant differences were also observed between AD/TTR+/− and AD/TTR−/−.

Figure 5: Effect of TTR genetic reduction in plasma Aβ1-42 and Aβ1-40 levels: Results are shown for 3-month old female mice with three distinct genotypes for TTR: AD/TTR+/+ (N = 5 for Aβ1-42; N = 4 for Aβ1-40), AD/TTR+/− (N = 6 for Aβ1-42; N = 4 for Aβ1-40) and AD/TTR−/− (N = 5 for Aβ1-42; N = 4 for Aβ1-40).

Taken together, our results suggest that TTR influences plasma Aβ by reducing its levels.

Effect of TTR in Aβ1-42 internalization by SAHep cells and primary hepatocytes

Aβ is known to also be delivered at the liver for degradation; therefore, we analysed the effect of TTR in FAM-Aβ1-42 internalization using the SAHep cell line. Uptake of Aβ1-42 peptide increased in the presence of h rTTR showing a positive correlation between Aβ uptake and h rTTR concentration, reaching a maximum of 70% when using 4.5–7.5 μg/mL of TTR in 3 hours (Fig. 6A).

Figure 6: Effect of TTR in Aβ peptide internalization by hepatocytes:

Figure 6

(A) FAM-Aβ1-42 internalization by SAHep cells, in the absence or presence of increasing concentrations of h rTTR, as measured by flow cytometry. TTR concentrations up to 4.5–7.5 μg/mL resulted in increased Aβ internalization by cells. N = 3 for each condition. (B) Flow cytometry of primary cultures of hepatocytes derived from mice with different genetic TTR backgrounds; hepatocytes derived from TTR+/+ mice showed significantly more internalization of FAM-Aβ1-42 than those derived from TTR+/− and from TTR−/−. N =  11, N = 8, N = 14, N = 6 for hepatocytes derived from TTR +/+, TTR +/−, TTR −/− and h rTTR treated TTR −/− mice, respectively. (C) moTTR levels in supernatants of primary hepatocytes measured by ELISA confirmed the genetic reduction in TTR+/− which showed about half of the TTR in TTR+/+, while TTR−/− produced no TTR protein. N = 7 for TTR+/+ and −/− mice and N = 5 for TTR +/−.

 

To further study the effect of TTR in Aβ1-42 uptake by hepatocytes, and in order to avoid addition of exogenous TTR (since hepatocytes produce TTR), we prepared primary cultures of hepatocytes derived from mice with different TTR genetic backgrounds (TTR+/+, TTR+/− and TTR−/−). TTR secretion was evaluated by ELISA revealing values of approximately 70 and 40 ng/mL for TTR+/+ and TTR+/−, respectively, over a period of 3 hours (Fig. 6C). TTR−/− hepatocytes did not produce TTR, as expected.

As for Aβ1-42 uptake, we observed that TTR facilitated peptide internalization by primary hepatocytes as differences were statistically significant between genetic backgrounds (Fig. 6B). Importantly, addition of h rTTR to TTR−/− hepatocytes partially rescued the phenotype as internalization values equalized those of TTR+/− cells.

Influence of TTR on LRP1 levels

We firstly assessed LRP1 expression by qRT-PCR in total brain extracts of TTR+/+, TTR+/− and TTR−/− mice, and observed significant differences in the expression of this receptor: brains from TTR+/+ mice expressed LRP1 in significantly higher levels than brains from TTR−/− animals (Fig. 7A1). These results were corroborated by measuring LRP1 protein levels by western blot (Fig. 7A2).

Figure 7: LRP1 expression in the brain, liver and cell lines assessed by qRT-PCR, western blot and immunofluorescence: LRP1 levels investigated in the brains from TTR+/+, TTR+/− and TTR−/− mice by

(A1) qRT-PCR (n = 4) and (A2) by western blot (n = 3), showed to correlate directly with TTR levels. hCMEC/D3 cells (n = 3) incubated with TTR showed higher amounts of (B1) mRNA and (B2) protein than cells without TTR. Similarly, livers of TTR+/+ mice expressed more LRP1, both (C1) mRNA (n = 4) and (C2) protein (n = 3), than of TTR−/− mice. (D1) qRT-PCR for LRP1 in SAHep cells incubated with exogenous h rTTR increased their LRP1 mRNA levels (n = 3). (D2) Upon incubation with TTR, SAHep cells increased their LRP1 protein levels.

To further understand the importance of TTR in regulating LRP1 levels in the context of Aβ transport across the BBB, we incubated hCMEC/D3 cells with h rTTR and investigated LRP1 expression by qRT-PCR. As depicted in Fig. 7B1, hCMEC/D3 incubated with TTR displayed higher LRP1 expression, thus confirming the regulation of LRP1 by TTR in these endothelial cells; these results were also corroborated by protein levels, as evaluated by immunocytochemistry (Fig. 7B2)

Similarly to the internalization studies, we also evaluated the ability of TTR to regulate LRP1 levels in hepatocytes by performing qRT-PCR studies in livers from TTR+/+, TTR+/− and TTR−/− mice, as well as in the hepatocyte cell line, SAHep cells. Similarly to the brains, livers from TTR+/+ mice expressed higher levels of LRP1, when compared to the livers from TTR−/− animals (Fig. 7C1). Protein analysis confirmed the effect of TTR at increasing LRP1 and as for the brains, significant differences were observed between TTR+/+ and TTR−/− mice (Fig. 7C2). As for the cell line, SAHep cells analyzed by qRT-PCR (Fig. 7D1) and immunocytochemistry (Fig. 7D2) showed increased LRP1 mRNA and protein levels, respectively, when incubated with TTR.

 

Altogether, these results indicate that TTR regulates LRP1 levels, suggesting that TTR uses this receptor to promote Aβ clearance.

TTR is a transporter protein mainly synthesized in the liver and in the CP of the brain and secreted into the blood and CSF, respectively. TTR is known to transport several molecules, in particular T4 and retinol through binding to the retinol binding protein (RBP). In the CSF, TTR binds Aβ peptide impeding its deposition in the brain. However, the molecular mechanism underlying this process is not known. Given our earlier evidences that TTR lowers brain and plasma Aβ11, we hypothesized that TTR could function as an Aβ carrier that transports the peptide to its receptor at the brain barriers and at the liver.

Since the cerebral capillaries represent about the double of the total apical surface area of the CP27, we decided to start by studying the effect of TTR in Aβ transport at the BBB. Using the hCMEC/D3 in vitro model of the BBB, we showed that TTR significantly increased Aβ internalization by these cells. Both in the presence and absence of TTR, Aβ internalization levels were high after 15 minutes and no significant increase was measured after 30 minutes. Thus, we assessed efflux by removing media with FAM-Aβ1-42 after a period of incubation to show that TTR was also promoting Aβ efflux from these cells.

To further study the effect of TTR in Aβ transport using the hCMEC/D3 model and given the differential expression of receptors in polarized BBB endothelial cells, we next performed our experiments using transwell cultures. Brain-to-blood transport of Aβ peptide was investigated and we concluded that TTR increased Aβ transport, if added to the brain side but not if added to the blood side. This observation is consistent with a direct TTR/Aβ interaction, as previously demonstrated28. To understand if TTR was also being transported while carrying Aβ, we also evaluated TTR ability to cross the endothelial monolayer to show that this protein can cross in the brain-to-blood direction, but does not cross in the opposite direction. To confirm this, we analyzed in vivo TTR brain permeability using TTR−/− mice injected with h rTTR either into the brain ventricle or into the tail vein. The presence of TTR was then investigated in brain and blood. The results corroborated the in vitroobservations since upon IC administration of TTR, the protein was rapidly found in blood; however, after IV injection of TTR the protein was detected neither in CSF nor in the brain extracts. Our findings are also supported by previous work on TTR turnover and degradation29; in this work authors reported that rat TTR injected intraventricularly into the CSF of rats was mainly degraded in the liver and kidneys (therefore effluxing from the brain), whereas no specific transfer of plasma TTR to the nervous system or degradation of plasma TTR in the nervous system was observed. It is worthy to note that Makover and colleagues injected purified rat TTR in a system containing the same endogenous rat TTR29, and results are similar to the ones we describe now. Therefore, we can conclude that in our system the TTR−/− background did not significantly affected TTR clearance.

The differential brain permeability to TTR indicates the use of a receptor with preferential expression on the basolateral membrane of the endothelial cells forming the BBB, such as LRP1, which in turn is known to internalize Aβ peptide. Whether TTR can cross or not as a complex, namely with Aβ peptide, is not known and needs to be investigated.

 

TTR gene expression in the brain is usually described as being confined to the CP and meninges, although TTR can be transported to other brain cells. For instance, it is described that in situations of compromised heat-shock response, and as a response to cerebral ischemia, CSF TTR contributes to control neuronal cell death, edema and inflammation12. This implies that TTR is transported from CSF to other brain areas, and thus it is also possible that this protein participates in Aβ transport at the BBB. TTR gene expression has been also attributed to neurons and for instance, SH-SY5Y cells transfected with APP695 isoform showed up-regulation of TTR mRNA expression, with concomitant decrease in Aβ levels16. Other authors showed that the majority of hippocampal neurons from human AD and all those from APP23 mouse brains contain TTR. In addition, quantitative PCR for TTR mRNA and Western blot analysis showed that primary neurons from APP23 mice transcribe TTR mRNA, and that the cells synthesize and secrete TTR protein15. More recently, it has been shown that TTR transcription and protein production can be induced by heat shock factor 1 (HSF1) in hippocampal neurons but not in the liver, both using cell lines and in vivo approaches17.

Importantly, the BCSFB should also be investigated for TTR-assisted Aβ transport, since this protein is the major protein binding Aβ in CSF. In spite of the low TTR levels in CSF (~2 mg/mL), the choroid plexus is presented as the major site of TTR expression, expressed as a ratio of TTR/mass of tissue, corresponding to a ~30-fold higher than that found in plasma30. Interestingly, a recent report describes that in a triple transgenic mouse model of AD only the Aβ1-42 isoform is increased at the epithelial cytosol, and in stroma surrounding choroidal capillaries. Noteworthy, there was increased expression, presumably compensatory, of the choroidal Aβ transporters: LRP1 and RAGE. In addition, authors reported that the expression of TTR was attenuated as compared to non-transgenic mice31.

Previous works indicated that the genetic reduction of TTR in an AD mouse model results in increased Aβ brain levels9,10; another work using 7 month old female mice also showed increased Aβ1-42 plasma levels in AD/TTR+/− mice as compared to age-and gender-matched AD/TTR+/+ animals. In the present work, we extended our study and evaluated both plasma Aβ1-42 and Aβ1-40 isoforms in 3 months old AD/TTR+/+, AD/TTR/+/− and AD/TTR−/− animals, showing that TTR correlates negatively with both isoforms of Aβ. Further, these findings support the idea that plasma may also reflect disease disturbances in AD.

Thus, the following level of our study focused on the effect of TTR in Aβ peptide uptake by the liver. After showing that h rTTR produces a concentration-dependent increase in Aβ internalization by SAHep cells, we worked with primary hepatocytes derived from mice with different TTR backgrounds showing again higher levels of internalization in the presence of TTR.

Interestingly, previous work has shown that TTR is internalized by the liver using a RAP-sensitive receptor20, such as LRP1. Multiple factors influence the function of LRP1-mediated Aβ clearance, such as its expression, shedding, structural modifications and transcriptional regulation by other genes32. Recent studies have clarified how Aβ clearance mechanisms in the CNS are indirectly altered by vascular and metabolism-related genes via the sterol regulatory element binding protein (SREBP2)33. In addition, AD risk genes such as phosphatidylinositol binding clathrin assembly protein (PICALM)34 and apoE isoforms can differentially regulate Aβ clearance from the brain through LRP135.

Consequently, given the importance of this receptor in Aβ clearance both from the brain and at the liver, we evaluated the levels of gene and protein expression in different models. Both LRP1 transcript and protein levels were increased in TTR+/+ brains as compared to TTR−/−. To further confirm the importance of TTR in regulating the levels of LRP1 specifically at the BBB, and contributing to explain the importance of TTR in Aβ clearance, we measured LRP1 in hCMEC/D3 cells with and without incubation with TTR. We observed that the presence of TTR clearly increased the receptor expression, producing significant differences. A similar study was then undertaken for liver and SAHep cells, which again showed regulation of LRP1 expression by TTR. Whether liver TTR regulates liver LRP1 and CSF TTR regulates brain LRP1 is not known and further studies, namely differential silencing of the TTR gene (liver or CP), should be performed.

In a recent study, TTR has been described to regulate insulin-like growth factor receptor I (IGF-IR) expression in mouse hippocampus (but not in choroid plexus) and this effect is due to TTR mainly synthesized by the choroid plexus (and secreted into the CSF) and not by peripheral TTR36. Once more, the possibility for local TTR production has been advanced by some authors16,17, as already mentioned. Finally, it is also known that LRP1 and IGF-IR interact37,38 in a way that the extracellular ligand-binding domain of LRP1 is not involved thus remaining free to bind its ligands. A common link is now established as TTR can regulate the expression of both receptors, albeit in different areas of the brain, opening the possibility for TTR being involved in other processes in the CNS. Moreover, using mice with deleted APP and APLP2, APP has been shown to down-regulate expression of LRP139 via epigenetic events mediated through its intracellular domain (AICD) and to up-regulate TTR, as previously described16. Though it is not known if LRP1 and TTR regulation are part of the same AICD-pathway since TTR levels were not evaluated in the APP and APLP2-deleted mice.

In summary, we show that neuroprotective effects of TTR previously observed in the context of AD are consistent with its role in Aβ clearance at the BBB and liver, and that TTR regulates LRP1 expression, suggesting that TTR is also transported by this receptor. In the future, the TTR-LRP1 cascade should be further investigated for therapeutic targeting.

Summary

TTR decreases in the population of both men and women after age 45 years.  This has consequences with respect to AD.  TTR is mainly synthesized by the choroid plexus (and secreted into the CSF) and not by peripheral TTR36, but this declines even earlier than that produced by the liver. (Ingenbleek and Bernstein, 2016).  This suggests a significant role for these age related changes in the development of AD.  Moreover, what has been presented indicates a role for snake venum in increasing the removal of amyloid plaque that develops in AD.  TTR is important in A-beta clearance in liver and BBB.  There was a shift in APP processing towards the amyloidogenic processing in vivo at the end of the 5-month treatment with Aβ1-6A2VTAT(D) that was not observed in shorter treatment schedules with the same compound

 

MIT scientists find evidence that Alzheimer’s ‘lost memories’ may one day be recoverable    By Ariana Eunjung Cha

https://www.washingtonpost.com/news/to-your-health/wp/2016/03/17/mit-scientists-find-evidence-that-alzheimers-lost-memories-may-one-day-be-recoverable/?tid=pm_national_pop_b

Scientists had assumed for a long time that the disease destroys how those memories are encoded and makes them disappear forever. But what if they weren’t actually gone — just inaccessible?

A new paper published Wednesday by the Massachusetts Institute of Technology’s Nobel Prize-winning Susumu Tonegawa provides the first strong evidence of this possibility and raises the hope of future treatments that could reverse some of the ravages of the disease on memory.

“The important point is, this is a proof of concept,” Tonegawa said. “That is, even if a memory seems to be gone, it is still there. It’s a matter of how to retrieve it.”

Zane JaunmuktaneSimon MeadMatthew Ellis, …., A. Sarah WalkerPeter RudgeJohn Collinge & Sebastian Brandner
Nature (10 Sep 2015)
;525,247–250     
     doi:10.1038/nature15369

More than two hundred individuals developed Creutzfeldt–Jakob disease (CJD) worldwide as a result of treatment, typically in childhood, with human cadaveric pituitary-derived growth hormone contaminated with prions1, 2. Although such treatment ceased in 1985, iatrogenic CJD (iCJD) continues to emerge because of the prolonged incubation periods seen in human prion infections. Unexpectedly, in an autopsy study of eight individuals with iCJD, aged 36–51 years, in four we found moderate to severe grey matter and vascular amyloid-β (Aβ) pathology. The Aβ deposition in the grey matter was typical of that seen in Alzheimer’s disease and Aβ in the blood vessel walls was characteristic of cerebral amyloid angiopathy3 and did not co-localize with prion protein deposition. None of these patients had pathogenic mutations, APOE ε4 or other high-risk alleles4associated with early-onset Alzheimer’s disease. Examination of a series of 116 patients with other prion diseases from a prospective observational cohort study5 showed minimal or no Aβ pathology in cases of similar age range, or a decade older, without APOE ε4 risk alleles. We also analysed pituitary glands from individuals with Aβ pathology and found marked Aβ deposition in multiple cases. Experimental seeding of Aβ pathology has been previously demonstrated in primates and transgenic mice by central nervous system or peripheral inoculation with Alzheimer’s disease brain homogenate6, 7, 8, 9, 10, 11. The marked deposition of parenchymal and vascular Aβ in these relatively young patients with iCJD, in contrast with other prion disease patients and population controls, is consistent with iatrogenic transmission of Aβ pathology in addition to CJD and suggests that healthy exposed individuals may also be at risk of iatrogenic Alzheimer’s disease and cerebral amyloid angiopathy. These findings should also prompt investigation of whether other known iatrogenic routes of prion transmission may also be relevant to Aβ and other proteopathic seeds associated with neurodegenerative and other human diseases.

http://www.nih.gov/news-events/news-releases/decoding-molecular-ties-between-vascular-disease-alzheimers

The research, described in the journal Nature, involved two groups of mice. One was a normal control and the other was  genetically engineered to have Alzheimer’s-like symptoms. Both groups were given a mild electric shock to their feet. The first group appeared to remember the trauma of the incident by showing fear when placed back in the box where they had been given the shock. The Alzheimer’s mice, on the other hand, seemed to quickly forget what happened and did not have an upset reaction to the box.

Their reaction changed dramatically when the scientists stimulated tagged cells in their brains in the hippocampus — the part of the brain that encodes short-term memories — with a special blue light. When they were put back in the box following the procedure, their memories of the shock appeared to have returned, and they displayed the same fear as their healthy counterparts.

Tonegawa and his colleagues wrote that the treatment appears to have boosted neurons to regrow small buds called dendritic spines that form connections with other cells.

 

The revelations have “shattered a 20-year paradigm of how we’re thinking about the disease,” Rudy Tanzi, a Harvard neurology professor who is not involved in the research, told the Boston Herald. He said that since the 1980s, researchers believed the memories just weren’t getting stored properly.

The technique used in the study — optical stimulation of brain cells, or “optogenetics” — involves the insertion of a gene into parts of a brain to make them sensitive to blue light and then stimulating them with the light.

In a commentary accompanying the paper, Prerana Shrestha and Eric Klann of the Center for Neural Science at New York University said that the research employed a “clever strategy” and that “the potential to rescue long-term memory in dementia is exciting.”

Doug Brown, director of research at the Alzheimer’s Society, cautioned that the technique is not something that can be translated into a procedure that is safe for the estimated 44 million people worldwide with dementia just yet.

“While interesting,” he told the Guardian, “the practicalities of this approach — using a special blue light to stimulate memory — means that we’re still many years away from knowing if it would be possible to restore lost memories in people.”

Electrical stimulation of the brain may be one alternative scientists can pursue, according to Christine Denny, a neurobiologist at Columbia University. Nature reported that early trials showed that deep-brain stimulation of the hippocampus may improve memory in some Alzheimer’s patients.

 

Memory retrieval by activating engram cells in mouse models of early Alzheimer’s disease

Dheeraj S. RoyAutumn AronsTeryn I. MitchellMichele PignatelliTomás J. Ryan Susumu Tonegawa
Nature(2016)
       doi:10.1038/nature17172

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive memory decline and subsequent loss of broader cognitive functions1. Memory decline in the early stages of AD is mostly limited to episodic memory, for which the hippocampus has a crucial role2. However, it has been uncertain whether the observed amnesia in the early stages of AD is due to disrupted encoding and consolidation of episodic information, or an impairment in the retrieval of stored memory information. Here we show that in transgenic mouse models of early AD, direct optogenetic activation of hippocampal memory engram cells results in memory retrieval despite the fact that these mice are amnesic in long-term memory tests when natural recall cues are used, revealing a retrieval, rather than a storage impairment. Before amyloid plaque deposition, the amnesia in these mice is age-dependent3, 4, 5, which correlates with a progressive reduction in spine density of hippocampal dentate gyrus engram cells. We show that optogenetic induction of long-term potentiation at perforant path synapses of dentate gyrus engram cells restores both spine density and long-term memory. We also demonstrate that an ablation of dentate gyrus engram cells containing restored spine density prevents the rescue of long-term memory. Thus, selective rescue of spine density in engram cells may lead to an effective strategy for treating memory loss in the early stages of AD.

Figure 1: Optogenetic activation of memory engrams restores fear memory in early AD mice

Optogenetic activation of memory engrams restores fear memory in early AD mice.

ac, Amyloid-β (Aβ) plaques in 9-month-old AD mice (a), in the DG (b), and in the EC (c). d, Plaque counts in HPC sections (n = 4 mice per group). ND, not detected. e, CFC behavioural schedule (n = 10 mice per group). fi, Freezing leve…

Figure 2: Neural correlates of amnesia in early AD mice.close

Neural correlates of amnesia in early AD mice.

a, b, Images showing dendritic spines from DG engram cells of control (a) and AD (b) groups. c, Average spine density showing a decrease in AD mice (n = 7,032 spines) compared with controls (n = 9,437 spines, n = 4 mice per group).

 

Behavioural rescue and spine restoration by optical LTP is protein-synthesis dependent.

Behavioural rescue and spine restoration by optical LTP is protein-synthesis dependent.

a, Modified behavioural schedule for long-term rescue of memory recall in AD mice in the presence of saline or anisomycin (left). Memory recall 2 days after LTP induction followed by drug administration showed less freezing of AD mice

 

Turn Off Alzheimer’s Disease

Lomonosov Moscow State University   http://www.dddmag.com/news/2016/03/turn-alzheimers-disease

This image shows the three-dimensional structure of the dimer of the metal-binding domain of beta-amyloid peptide having 'English mutation'. Two peptide molecules connected to each other with the help of zinc ion. Source: This image shows the three-dimensional structure of the dimer of the metal-binding domain of beta-amyloid peptide having 'English mutation'.  Source: Lomonosov Moscow State University

This image shows the three-dimensional structure of the dimer of the metal-binding domain of beta-amyloid peptide having ‘English mutation’. Two peptide molecules connected to each other with the help of zinc ion. Source: This image shows the three-dimensional structure of the dimer of the metal-binding domain of beta-amyloid peptide having ‘English mutation’. Source: Lomonosov Moscow State University

A group of the Lomonosov Moscow State University scientists, together with their colleagues from the Institute of Molecular Biology, Russian Academy of Sciences and the King’s College London, succeeded in sorting out the mechanism of Alzheimer’s disease development and possibly distinguished its key trigger. Their article was published in Scientific Reports.

‘Alzheimer’s disease is a widespread degenerative damage of central nervous system leading to a loss of mental ability.’Until now it was considered incurable,’ tells Vladimir Polshakov, the leading researcher, MSU Faculty of Fundamental Medicine. Though now scientists managed to distinguish the mechanism ‘running’ the disease development, so, a chance appeared to elaborate some new chemical compounds, that may work as an efficient cure.

Several hypotheses are dedicated to the Alzheimer’s disease development. One of the most common is the so-called amyloid hypothesis.

Amyloids (to be precise, beta-amyloid peptides) are molecular constructions of a protein type and in its normal healthy state they provide a protection to the brain cells. They live fast, and having fulfilled their function they fall prey to the work of proteases, the cleaning enzymes that cut all the used protein elements into harmless ‘slags’ that are further reclaimed or removed from a body. However, according to the amyloid hypothesis, at some point something goes wrong, and the cells’ protectors turn to be their killers. Moreover, those peptides start gathering, forming aggregations and hence getting out of the reach of proteases’ cutting blades. Within the amyloid hypothesis this mechanism is more or less precisely described on the later stages of the disease, when the toxic aggregations appeared already and further, when the brain is covered with amyloid plaques. However, the early stage of a beta-amyloid transformation into harmful organic products is highly unexplored.

‘We knew, for example, that a crucial role in initiation of such processes is played by ions of several transition metals, first of all — zinc,’ tells Vladimir Polshakov. ‘Zinc actually conducts a number of useful and healthy functions in a brain, though in this case it was reasonably suspected as a ‘pest’, and particularly as an initiator of a cascade of processes, leading to theAlzheimer’sdisease. However, it remained unclear, what exactly happens during an interaction of zin? ions with peptide molecules, which amino acids bind zinc ions, and how such interaction stipulates a peptide aggregation. We set a goal to clarify at least some of those questions’.

Scientists studied various pathogenic beta-amyloid peptides, their so-called metal binding domains — relatively short peptide regions, capable to bind metal ions. A number of experimental techniques were applied, including nuclear magnetic resonance (NMR) spectroscopy, used to determine the structure of the forming molecular complexes. Some spectra requiring higher sensitivity were additionally measured in London. According to Polshakov, the choice of the studied pathogens was ‘partly a luck’. One of the specimens was the product of so-called ‘English mutation’ — peptide, different from a common beta-amyloid peptide only with one amino acid substitution. Using the NMR spectroscopy scientists managed to sort out chemical processes and structural changes while a peptide molecules interact with zinc ion and undergo further aggregation.

The second pathogen was an isomerized beta-amyloid peptide. It was not different from a normal one in its chemical composition, though one of its amino acid residues, aspartic acid, was in a form with a specific atomic positioning. Such isomerism happens spontaneously, without help of any enzymes, and is related to the ageing processes, another influential factor of the Alzheimer’s disease. Fellow biologists from the Moscow’s Institute of Molecular Biology showed recently, that administration of an isomerized peptide to transgenic mice led to an accelerated formation of amyloid plaques. With the presence of zinc ions, a metal binding domain of the isomerized peptide aggregated so fast that the forming structures were hard to detect. Though scientists managed to distinguish that despite all the differences in processes occurring to the ‘English mutant’ and isomerized peptide in presence of zinc ions, initial stages of these transformations were similar. The trigger happened to be the same — a role of a pathogenic aggregation’s seed was in both cases played by initially formed peptide dimers, i.e. two peptide molecules, connected to each other with help of zinc ion. Such dimers were also detected in normal human peptides, and the difference in all the studied forms could be explained by the speed of formation of corresponding dimer and its proneness to a further aggregation.

Based on their findings, researches proposed the mechanism of zinc-controlled transformation of a peptide-protector into a peptide-killer. That mechanism, scientists notice, explains multiple experimental data, not only gathered by the group, but also collected by their colleagues in other laboratories preoccupied with the Alzheimer’s disease studies. Researchers also hope that thanks to a very certain targeting their discovery would help to produce new medicine capable to block beta-amyloid peptide aggregation stipulated by zinc ions.

 

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Breakup of amyloid plaques

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

 

Small Molecule EPPS Breaks Up Amyloid Plaques

Alzheimers Plaque Therapy, Alzheimers small molecule, amyloid plaque treatment

One of the hallmarks of Alzheimer’s disease has been the generation of Amyloid-β (Aβ) oligomers, fibrils, and ultimately plaques. It is currently contended whether these plaques are a cause of Alzheimer’s disease and related mental deficits, or merely an effect. Researchers at the Korea Institute of Science and Technology have demonstrated in vivo formation and disaggregation of Aβ plaques. They previously reported small ionic molecules which could accelerate the formation of Aβ plaques. Six small molecules which inhibited aggregate formation were discovered at the same time. One of these molecules, 4-(2-hydroxyethyl)-1-piperazinepropanesulphonic acid (EPPS), works as a therapeutic in a Alzheimer’s mouse model. EPPS was found to be both orally available and cross the blood brain barrier where it directly binds to Aβ plaques. Double transgenic mice , APPswe/PS1-dE9 (amyloid precursor protein/presenilin protein 1) mice were administered EPPS in their drinking water for 3.5 months and compared to non-treated transgenic controls. EPPS treated mice both improved from their baseline and out-performed transgenic controls in both the Morris water maze and contextual fear response tests. Immunofluorescent staining of matched brain regions demonstrated elimination of Aβ plaques in the hippocampus of EPPS treated mice. Further study is required to completely understand the mechanism by which EPPS disaggregates the Aβ plaques. This study demonstrates the cause and effects Aβ plaque generation, and subsequent removal, has on Alzheimer’s disease related cognitive function. Should the effect transfer to humans, this could prove a significant discovery for the treatment of Alzheimer’s disease.

 

Kim, et al. (October, 2015) EPPS rescues hippocampus-dependent cognitive deficits in APP/PS1 ice by disaggregation of amyloid-b oligomers and plaques Nature Communications

 

EPPS  rescues hippocampus-dependent cognitive deficits in APP/PS1 mice by disaggregation of amyloid-β oligomers and plaques

Hye Yun KimHyunjin Vincent KimSeonmi JoC. Justin LeeSeon Young ChoiDong Jin Kim & YoungSoo Kim

Nature Communications 2016; 6(8997)     http://dx.doi.org:/10.1038/ncomms9997

Alzheimer’s disease (AD) is characterized by the transition of amyloid-β (Aβ) monomers into toxic oligomers and plaques. Given that Aβ abnormality typically precedes the development of clinical symptoms, an agent capable of disaggregating existing Aβ aggregates may be advantageous. Here we report that a small molecule, 4-(2-hydroxyethyl)-1-piperazinepropanesulphonic acid (EPPS), binds to Aβ aggregates and converts them into monomers. The oral administration of EPPS substantially reduces hippocampus-dependent behavioural deficits, brain Aβ oligomer and plaque deposits, glial γ-aminobutyric acid (GABA) release and brain inflammation in an Aβ-overexpressing, APP/PS1 transgenic mouse model when initiated after the development of severe AD-like phenotypes. The ability of EPPS to rescue Aβ aggregation and behavioural deficits provides strong support for the view that the accumulation of Aβ is an important mechanism underlying AD.

 

During Alzheimer’s disease (AD) pathogenesis, amyloid-β (Aβ) monomers aberrantly aggregate into toxic oligomers, fibrils and eventually plaques. The concentration of misfolded Aβ species highly correlates with the severity of neurotoxicity and inflammation that leads to neurodegeneration in AD1, 2, 3. Accordingly, substantial efforts have been devoted to reducing Aβ levels, including methods to prevent the production and aggregation of Aβ4, 5, 6, 7. Although these approaches effectively prevent the de novo formation of Aβ aggregates, existing Aβ oligomers and plaques will still remain in the patient’s brain8, 9, 10. Thus, the desirable effects of Aβ inhibitors may be expected when administered before a patient develops toxic Aβ deposits5, 6, 7. However, in AD patients with mild-to-moderate symptoms, anti-amyloidogenic agents have not yielded expected outcomes, which may be due to the incomplete removal of pre-existing Aβ aggregates11. As Aβ typically begins to aggregate long before the onset of AD symptoms, interventions specifically aimed at disaggregating existing plaques and oligomers may constitute a useful approach to AD treatment, perhaps in parallel with agents aimed at inhibiting aggregate formation8, 9, 10, 11, 12.

 

Result highlights  

EPPS reduces Aβ-aggregate-induced memory deficits in mice

Figure 1: EPPS ameliorates Aβ-induced memory deficits in mice.

 

EPPS ameliorates A[beta]-induced memory deficits in mice.

(a) Time course of the experiments. (b) Intracerebroventricular (i.c.v.) injection site brain schematic diagram. (c) Pretreated effects of EPPS on Aβ-aggregate-induced memory deficits observed by the % alternation on the Y-maze. EPPS, 0 (n=10), 30 (n=9) or 100mgkg−1 per day (n=10), was orally given to 8.5-week-old ICR male mice for 1 week; then, vehicle (10% DMSO in PBS, n=10) or Aβ aggregates (50pmol per 10% DMSO in PBS; Supplementary Fig. 1A) were injected into the intracerebroventricular region (P=0.022). (d) Co-treated effects of EPPS on Aβ-aggregate-induced memory deficits observed by the % alternation on the Y-maze. Male, 8.5-week-old ICR mice received an injection of vehicle (n=9) or Aβ aggregates into the intracerebroventricular region, and then EPPS, 0 (n=10), 30 (n=10) or 100mgkg−1 per day (n=10), was orally given to these mice for 5 days. From the top, P=0.003, 0.006, 0.015. The error bars represent the s.e.m. One-way analysis of variance followed by Bonferroni’s post-hoc comparisons tests were performed in all statistical analyses. (*P<0.05, **P<0.01, ***P<0.001; other comparisons were not significant).

 

EPPS is orally safe and penetrates the blood–brain barrier

Orally administered EPPS rescues cognitive deficits in APP/PS1 mice

 

Figure 2: EPPS rescues hippocampus-dependent cognitive deficits.

http://www.nature.com/ncomms/2015/151208/ncomms9997/images_article/ncomms9997-f2.jpg

 

Figure 3: EPPS does not affect synaptic plasticity in mice.

http://www.nature.com/ncomms/2015/151208/ncomms9997/images_article/ncomms9997-f3.jpg

 

Figure 4: EPPS disaggregates Aβ plaques and oligomers in APP/PS1 mice.

EPPS disaggregates A[beta] plaques and oligomers in APP/PS1 mice.

APP/PS1 mice and WTs from the aforementioned behavioural tests were killed and subjected to brain analyses. EPPS, 0 (TG(), male, n=15), 10 (TG(+), male, n=11) or 30mgkg-1 per day (TG(++), male,n=8), was orally given to 10.5-month-old APP/PS1 for 3.5 months and their brains were compared with age-matched WT brains (WT(), male, n=16). (a) ThS-stained Aβ plaques in whole brains (scale bars, 1mm) and the hippocampal region (scale bars, 200μm) of each group. The mouse brain schematic diagram was created by authors (green and red boxes: regions of brain images, a and f, respectively). (b) Number or area of plaques normalized (%) to the level in 10.5-month-old TG mice. Plaque number: P-values compared with TG (male, 10.5-month-old) are all <0.0001 (#). P-values compared with TG() (male, 14-month-old) are all <0.0001 (*). Plaque area: P-values compared with TG (male, 10.5-month-old) are all <0.0001 (#). P-values compared with TG() (male, 14-month-old) are all <0.0001 (*). (ce) Aβ-insoluble and -soluble fractions analyses from brain lysates. (c) Sandwich ELISA of Aβ-insoluble fractions. Hippocampus: all P<0.0001; cortex: P=0.004, 0.046. (d) Sandwich ELISA of Aβ-soluble fractions. (e) Dot blotting of the total Aβ (anti-Aβ: 6E10, also recognizes APP) and oligomers (anti-amyloidogenic protein oligomer: A11). (f) Histochemical analyses of Aβ deposition. Aβs were stained with the 6E10 antibody and ThS. Aβ plaques (first row): green; all Aβs (second row): red; 4,6-diamidino-2-phenylindole (DAPI): blue (as a location indicator). The third and bottom rows show merged images of plaques and Aβs, and plaques and Aβs with DAPI staining. Scale bars, 50μm. (g) Western blotting analyses of APP expression in hippocampal and cortical lysates (detected at ~100kDa by 6E10 antibody). Densitometry (see Supplementary Fig. 3A). Full version (see Supplementary Fig. 7). The error bars represent the s.e.m. One-way analysis of variance followed by Bonferroni’s post-hoc comparisons tests were performed in all statistical analyses (*P<0.05, **P<0.01, ***P<0.001, #P<0.05, ##P<0.01,###P<0.001; other comparisons were not significant).

 

EPPS removes Aβ plaques and oligomers in APP/PS1 mice

Collectively, these results indicate that EPPS rescues hippocampus-dependent cognitive deficits when orally administered to aged, symptomatic APP/PS1 TG mice.

Collectively, these results indicate that orally administered EPPS effectively decreases Aβ plaques and oligomers in APP/PS1 model mouse brains.

 

EPPS lowers Aβ-dependent inflammation and glial GABA release

Figure 5: EPPS lowers inflammation and glial GABA release.

EPPS disaggregates Aβ oligomers and fibrils by direct interaction and reduces cytotoxicity

Figure 6: EPPS disaggregates Aβ aggregates by selective binding.

 

(1) a small molecule, EPPS, converts neurotoxic oligomers and plaques into non-toxic monomers by directly binding to Aβ aggregates;

(2) orally administered EPPS produces a dose-dependent reduction of Aβ plaque deposits and behavioural deficits in APP/PS1 TG mice, even when administration was delayed until after the pathology was well established;

(3) the beneficial effect of EPPS probably operates through an Aβ-related mechanism rather by facilitating cognitive processes; and

(4) large doses of EPPS appeared to be well tolerated in initial toxicity studies6, 7, 33.

Dr. T. Ronald Theodore
Email rtheodore@integratedbiologics.com
URL http://www.integratedbiologics.com
In Response To Breakup of amyloid plaques
Submitted on 2016/05/18 at 3:33 am
Comment Re: “EPPS rescues hippocampus-dependent cognitive deficits in APP/PS1 mice by disaggregation of amyloid-β oligomers and plaques” Kim et al, Nature Communications 8 December 2015
HEPES, Zwitterions, and the “Good” Buffers as Biological Response Modifiers

In reference to the article “EPPS rescues hippocampus-dependent cognitive deficits in APP/PS1 mice by disaggregation of amyloid-β oligomers and plaques” Kim et al, Nature Communications 8 December 2015, we note some important omissions.

Kim et al state specific effects of EPPS affecting Alzheimer’s disease. We would point out that EPPS is also referenced as HEPPS.1 HEPPS has been accepted as a “Good” buffer and a zwitterion. The authors attribute the effects of EPPS to anti-inflammatory action. The authors omit reference that EPPS (HEPPS) is a listed “Good” buffer and a zwitterion.1 The anti-inflammatory effects of zwitterions and “Good” buffers have been previously described.3,4 The effects of these zwitterions as biological response modifiers with effects on neurological diseases including Alzheimer’s have been previously noted.4,5 ( HEPES has been used preferentially based on Good’s original data showing HEPES has the highest ability to increase the rate of mitochondrial oxidative phosphorylation). Kim et al attribute the effects of EPPS to anti-inflammatory actions. The anti-inflammatory effects of the buffers are well known.3,4 We would suggest that anti-inflammatory effects of the buffers may be singular, synergistic or combined effects of other biological responses that have been noted including mitochondrial and other actions.4,5,6,7 Prior literature and data would certainly anticipate the findings of Kim et al. It is noted that all these zwitterionic buffers have effects on the neurological system.

What is important is that further research to determine the effects of these zwitterionic buffers as biological response modifiers on neurological diseases including Alzheimer’s is continued. The ability of the zwitterionic buffers on brain and other organ injury are currently under review.

T. Ronald Theodore
Integrated Biologics, LLC
rtheodore@integratedbiologics.com

1. Merck Index, 15th Edition, Feb 2015.
2. Norman E. Good et al., Hydrogen Ion Buffers for Biological Research, Biochemistry vol.5, No. 2, Feb. 1966.
3. “Effects of In-vivo Administration of Taurine and HEPES on the Inflammatory Response in Rats” Pharmacy and Pharmacology, vol. 46, No. 9, Sept. 1994.
4. Theodore et al., Zwitterionic Compositions and Methods as Biological Response Modifiers, US Patent No. 6,071,919.
5. Garvey et al., Phosphate and HEPES buffers potently affect the fibrillation and oligomerization mechanism of Alzheimer’s Aβ peptide, Biochemical and Biophysical Research Communications, 06/2011; 409(3):385-8. DOI: 10.1016/j.bbrc.2011.04.141.
6. Theodore et al., Pilot Ascending Dose Tolerance Study of Parenterally Administered 4-(2 Hydroxyethyl)-l-piperazine Ethane Sulfonic Acid (TVZ-7) in Dogs, Cancer Biotherapy & Radiopharmaceuticals, Volume 12, Number 5, 1997.
7. Theodore et al., Preliminary Evaluation of a Fixed Dose of Zwitterionic Piperazine (TVZ-7) in Clinical Cancer, Cancer Biotherapy and Radiopharmaceuticals, Volume 12, Number 5, 1997.

 

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Breakthrough Prize for Alzheimer’s Disease 2016

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Breakthrough Prize given for Alzheimer’s research

Since 2013 an annual prize has been awarded for the most significant breakthrough in research major sciences. This year the prize has gone to research into Alzheimer’s disease. Backed by big companies, the prize is now worth $3 million.

The focus on one single scientist is captured in the Prize’s mission statement, which runs: “Knowledge is humanity’s greatest asset. It defines our nature, and it will shape our future. The body of knowledge is assembled over centuries. Yet a single mind can extend it immensely.”

The 2016 award has been given to neuroscientist Dr. John Hardy for his research into Alzheimer’s disease. Hardy received his award at a ceremony held at NASA’s Ames Research Center in Mountain View, California. The master of ceremonies was comedian Seth McFarlane.

Dr. Hardy’s research has focused on the underlying genetic causes of Alzheimer’s disease. His most pioneering work has been with specific gene mutations that are connected with the disease. This was drawn from a study of a family in the U.K. where Alzheimer’s disease was disproportionately common. He found that chromosome 21 encoded an amyloid precursor protein, and that amyloid pathology is connected to Alzheimer’s disease. Hardy’s laboratory undertook full gene sequencing and identified a causative mutation.

Dr. Hardy later undertook research into the gene mutation and a protein termed tau, examining the connections here and the destruction of brain cells.

Read more: http://www.digitaljournal.com/life/health/breakthrough-prize-given-for-alzheimer-s-research/article/456063#ixzz3zPwerECU

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