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Archive for the ‘Pharmacotherapy and Cell Activity’ Category


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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Download references

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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Immunoediting can be a constant defense in the cancer landscape


Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

There are many considerations in the cancer immunoediting landscape of defense and regulation in the cancer hallmark biology. The cancer hallmark biology in concert with key controls of the HLA compatibility affinity mechanisms are pivotal in architecting a unique patient-centric therapeutic application. Selection of random immune products including neoantigens, antigens, antibodies and other vital immune elements creates a high level of uncertainty and risk of undesirable immune reactions. Immunoediting is a constant process. The human innate and adaptive forces can either trigger favorable or unfavorable immunoediting features. Cancer is a multi-disease entity. There are multi-factorial initiators in a certain disease process. Namely, environmental exposures, viral and / or microbiome exposure disequilibrium, direct harm to DNA, poor immune adaptability, inherent risk and an individual’s own vibration rhythm in life.

 

When a human single cell is crippled (Deranged DNA) with mixed up molecular behavior that is the initiator of the problem. A once normal cell now transitioned into full threatening molecular time bomb. In the modeling and creation of a tumor it all begins with the singular molecular crisis and crippling of a normal human cell. At this point it is either chop suey (mixed bit responses) or a productive defensive and regulation response and posture of the immune system. Mixed bits of normal DNA, cancer-laden DNA, circulating tumor DNA, circulating normal cells, circulating tumor cells, circulating immune defense cells, circulating immune inflammatory cells forming a moiety of normal and a moiety of mess. The challenge is to scavenge the mess and amplify the normal.

 

Immunoediting is a primary push-button feature that is definitely required to be hit when it comes to initiating immune defenses against cancer and an adaptation in favor of regression. As mentioned before that the tumor microenvironment is a “mixed bit” moiety, which includes elements of the immune system that can defend against circulating cancer cells and tumor growth. Personalized (Precision-Based) cancer vaccines must become the primary form of treatment in this case. Current treatment regimens in conventional therapy destroy immune defenses and regulation and create more serious complications observed in tumor progression, metastasis and survival. Commonly resistance to chemotherapeutic agents is observed. These personalized treatments will be developed in concert with cancer hallmark analytics and immunocentrics affinity and selection mapping. This mapping will demonstrate molecular pathway interface and HLA compatibility and adaptation with patientcentricity.

References:

 

https://www.linkedin.com/pulse/immunoediting-cancer-landscape-john-catanzaro/

 

https://www.cell.com/cell/fulltext/S0092-8674(16)31609-9

 

https://www.researchgate.net/publication/309432057_Circulating_tumor_cell_clusters_What_we_know_and_what_we_expect_Review

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4190561/

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5840207/

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5593672/

 

https://www.frontiersin.org/articles/10.3389/fimmu.2018.00414/full

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5593672/

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4190561/

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4388310/

 

https://www.linkedin.com/pulse/cancer-hallmark-analytics-omics-data-pathway-studio-review-catanzaro/

 

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Alnylam Announces First-Ever FDA Approval of an RNAi Therapeutic, ONPATTRO™ (patisiran) for the Treatment of the Polyneuropathy of Hereditary Transthyretin-Mediated Amyloidosis in Adults

Reporter: Aviva Lev-Ari, PhD, RN

Aug 10,2018

− First and Only FDA-approved Treatment Available in the United States for this Indication –

− ONPATTRO Shown to Improve Polyneuropathy Relative to Placebo, with Reversal of Neuropathy Impairment Compared to Baseline in Majority of Patients –

− Improvement in Specified Measures of Quality of Life and Disease Burden Demonstrated Across Diverse, Global Patient Population –

− Alnylam to Host Conference Call Today at 3:00 p.m. ET. −

CAMBRIDGE, Mass.–(BUSINESS WIRE)–Aug. 10, 2018– Alnylam Pharmaceuticals, Inc. (Nasdaq: ALNY), the leading RNAi therapeutics company, announced today that the United States Food and Drug Administration (FDA) approved ONPATTRO™ (patisiran) lipid complex injection, a first-of-its-kind RNA interference (RNAi) therapeutic, for the treatment of the polyneuropathy of hereditary transthyretin-mediated (hATTR) amyloidosis in adults. ONPATTRO is the first and only FDA-approved treatment for this indication. hATTR amyloidosis is a rare, inherited, rapidly progressive and life-threatening disease with a constellation of manifestations. In addition to polyneuropathy, hATTR amyloidosis can lead to other significant disabilities including decreased ambulation with the loss of the ability to walk unaided, a reduced quality of life, and a decline in cardiac functioning. In the largest controlled study of hATTR amyloidosis, ONPATTRO was shown to improve polyneuropathy – with reversal of neuropathy impairment in a majority of patients – and to improve a composite quality of life measure, reduce autonomic symptoms, and improve activities of daily living.

ONPATTRO was reviewed by the FDA under Priority Review and had previously been granted Breakthrough Therapy and Orphan Drug Designations. On July 27, patisiran received a positive opinion from the Committee for Medicinal Products for Human Use (CHMP) for the treatment of hereditary transthyretin-mediated amyloidosis in adults with stage 1 or stage 2 polyneuropathy under accelerated assessment by the European Medicines Agency. The recommended Summary of Product Characteristics (SmPC) for the European Union (EU) includes data on secondary and exploratory endpoints. Expected in September, the European Commission will review the CHMP recommendation to make a final decision on marketing authorization, applicable to all 28 EU member states, plus Iceland, Liechtenstein and Norway. Regulatory filings in other markets, including Japan, are planned beginning in mid-2018.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20180810005398/en/

 

INTELLECTUAL PROPERTY

Alnylam protects its Intellectual Property (IP) with fundamental, chemistry, delivery, and target patents and patent applications covering the development and commercialization of RNAi therapeutics as well as that afforded by the various trademark, copyright, and trade secret laws.

Alnylam’s patent estate includes a large number of issued patents and pending patent applications in the world’s major pharmaceutical markets—United States, European Union, and Japan, along with other countries throughout the world. This broad portfolio covers, for example, oligonucleotides, including synthetic RNA molecules, both modified and unmodified, optimized for a variety of delivery modalities, such as lipid- and conjugate-based systems, their synthesis and use, including use as therapeutics, diagnostics, and research reagents. We believe these patents and pending applications place Alnylam in the strongest possible position to not only build our company over the long term and accelerate our efforts to bring life-saving drugs to patients in need, but to enable other companies for advancement of RNAi therapeutics with licenses to our IP estate and associated know-how. This belief has been validated by the progress of Alnylam to date with multiple programs in pre-clinical and clinical development and with well over 30 distinct agreements entered into with leading pharmaceutical, biotechnology, and research reagent companies.

Alnylam has an extensive array of registered trademarks in the United States, European Union, Japan and other countries throughout the world as well as various copyrighted works. In addition to patent protection, Alnylam further safeguards its IP through the use of trade secret protection afforded by the relevant state and federal trade secret laws.

SOURCE

http://www.alnylam.com/our-science/intellectual-property/

Post       : Patisiran

URL        : http://newdrugapprovals.org/2018/08/13/patisiran/

Posted     : August 13, 2018 at 9:51 am

Author     : DR ANTHONY MELVIN CRASTO Ph.D

Tags       : 50FKX8CB2Y, 6024128, ALN-18328, ALN-TTR02, Alnylam

Pharmaceuticals, BREAKTHROUGH THERAPY, FAST TRACK, FDA 2018,

GENZ-438027, Onpattro, Orphan Drug Designation, patisiran, Priority

review, SAR-438037

Categories : 0rphan drug status, Breakthrough Therapy Designation,

FAST TRACK FDA, FDA 2018, Priority review

https://upload.wikimedia.org/wikipedia/commons/thumb/b/ba/Patisiran.png/60

0px-Patisiran.png

Patisiran

Sense strand:

https://integrity.thomson-pharma.com/integrity/img//en/vspacer_en.gif

GUAACCAAGAGUAUUCCAUdTdT

https://integrity.thomson-pharma.com/integrity/img//en/vspacer_en.gif

Anti-sense strand:

https://integrity.thomson-pharma.com/integrity/img//en/vspacer_en.gif

AUGGAAUACUCUUGGUUACdTdT

RNA, (A-U-G-G-A-A-Um-A-C-U-C-U-U-G-G-U-Um-A-C-dT-dT), complex with RNA

(G-Um-A-A-Cm-Cm-A-A-G-A-G-Um-A-Um-Um-Cm-Cm-A-Um-dT-dT) (1:1),

ALN-18328, 6024128  , ALN-TTR02  , GENZ-438027  , SAR-438037  ,

50FKX8CB2Y (UNII code)

for RNA, (A-U-G-G-A-A-Um-A-C-U-C-U-U-G-G-U-Um-A-C-dT-dT), complex

with RNA(G-Um-A-A-Cm-Cm-A-A-G-A-G-Um-A-Um-Um-Cm-Cm-A-Um-dT-dT) (1:1)

Nucleic Acid Sequence

Sequence Length: 42, 21, 2112 a 7 c 7 g 4 t 12 umultistranded (2);

modified

CAS 1420706-45-1

Treatment of Amyloidosis,

SEE…..https://endpts.com/gung-ho-alnylam-lands-historic-fda-ok-on-patisi

ran-revving-up-the-first-global-rollout-for-an-rnai-breakthrough/

Lipid-nanoparticle-encapsulated double-stranded siRNA targeting a 3

untranslated region of mutant and wild-type transthyretin mRNA

Patisiran (trade name Onpattro®) is a medication for the treatment of

polyneuropathy ( https://en.wikipedia.org/wiki/Polyneuropathy )  in

people with hereditary transthyretin-mediated amyloidosis (

https://en.wikipedia.org/wiki/Hereditary_transthyretin-mediated_amyloidosi

s

) . It is the first small interfering RNA (

https://en.wikipedia.org/wiki/Small_interfering_RNA ) -based drug

approved by the FDA ( https://en.wikipedia.org/wiki/FDA ) . Through

this mechanism, it is a gene silencing (

https://en.wikipedia.org/wiki/Gene_silencing )  drug that interferes

with the production of an abnormal form of transthyretin (

https://en.wikipedia.org/wiki/Transthyretin ) .

https://upload.wikimedia.org/wikipedia/commons/thumb/b/ba/Patisiran.png/60

0px-Patisiran.png

( https://en.wikipedia.org/wiki/File:Patisiran.png )

Chemical structure of Patisiran.

During its development, patisiran was granted orphan drug status (

https://en.wikipedia.org/wiki/Orphan_drug_status ) , fast track

designation ( https://en.wikipedia.org/wiki/Fast_track_designation ) ,

priority review ( https://en.wikipedia.org/wiki/Priority_review )  and

breakthrough therapy designation (

https://en.wikipedia.org/wiki/Breakthrough_therapy_designation )  due

to its novel mechanism and the rarity of the condition it is designed

to treat.[1] ( https://en.wikipedia.org/wiki/Patisiran#cite_note-1 )

[2] ( https://en.wikipedia.org/wiki/Patisiran#cite_note-2 )  It was

approved by the FDA in August 2018 and is expected to cost around

$345,000 to $450,000 per year.[3] (

https://en.wikipedia.org/wiki/Patisiran#cite_note-3 )

Patisiran was granted orphan drug designation in the U.S. and Japan

for the treatment of familial amyloid polyneuropathy. Fast track

designation was also granted in the U.S. for this indication. In the

E.U., orphan drug designation was assigned to the compound for the

treatment of transthyretin-mediated amyloidosis (initially for the

treatment of familial amyloid polyneuropathy)

Hereditary transthyretin-mediated amyloidosis (

https://en.wikipedia.org/wiki/Hereditary_transthyretin-mediated_amyloidosi

s

)  is a fatal rare disease (

https://en.wikipedia.org/wiki/Rare_disease )  that is estimated to

affect 50,000 people worldwide. Patisiran is the first drug approved

by the FDA to treat this condition.[4] (

https://en.wikipedia.org/wiki/Patisiran#cite_note-4 )

Patisiran is a second-generation siRNA therapy targeting mutant

transthyretin (TTR) developed by Alnylam for the treatment of familial

amyloid polyneuropathy. The product is delivered by means of Arbutus

Biopharma’s (formerly Tekmira Pharmaceuticals) lipid nanoparticle

technology

https://endpts.com/wp-content/uploads/2018/08/GettyImages-902989426.jpg

“A lot of peo­ple think it’s win­ter out there for RNAi. But I think

it’s spring­time.” — Al­ny­lam CEO John Maraganore, NYT, Feb­ru­ary 7,

2011.

Patisiran — designed to silence messenger RNA and block the production

of TTR protein before it is made — is number 6 on Clarivate’s list of

blockbusters (

https://endpts.com/12-blockbusters-the-surging-list-of-1b-plus-drugs-rolli

ng-out-on-the-market-this-year-might-surprise-you/

)  set to launch this year, with a 2022 sales forecast of $1.22

billion. Some of the peak sales estimates range significantly higher

as analysts crunch the numbers on a disease that afflicts only about

30,000 people worldwide.

PATENT

WO 2016033326

https://patents.google.com/patent/WO2016033326A2

Transthyretin (TTR) is a tetrameric protein produced primarily in the

liver.

Mutations in the TTR gene destabilize the protein tetramer, leading to

misfolding of monomers and aggregation into TTR amyloid fibrils

(ATTR). Tissue deposition results in systemic ATTR amyloidosis

(Coutinho et al, Forty years of experience with type I amyloid

neuropathy. Review of 483 cases. In: Glenner et al, Amyloid and

Amyloidosis, Amsterdam: Excerpta Media, 1980 pg. 88-93; Hou et al.,

Transthyretin and familial amyloidotic polyneuropathy. Recent progress

in understanding the molecular mechanism of

neurodegeneration. FEBS J 2007, 274: 1637-1650; Westermark et al,

Fibril in senile systemic amyloidosis is derived from normal

transthyretin. Proc Natl Acad Sci USA 1990, 87: 2843-2845). Over 100

reported TTR mutations exhibit a spectrum of disease symptoms.

[0004] TTR amyloidosis manifests in various forms. When the peripheral

nervous system is affected more prominently, the disease is termed

familial amyloidotic

polyneuropathy (FAP). When the heart is primarily involved but the

nervous system is not, the disease is called familial amyloidotic

cardiomyopathy (FAC). A third major type of TTR amyloidosis is called

leptomeningeal/CNS (Central Nervous System) amyloidosis.

[0005] The most common mutations associated with familial amyloid

polyneuropathy (FAP) and ATTR-associated cardiomyopathy, respectively, are Val30Met

(Coelho et al, Tafamidis for transthyretin familial amyloid

polyneuropathy: a randomized, controlled trial. Neurology 2012, 79:

785-792) and Vall22Ile (Connors et al, Cardiac amyloidosis in African

Americans: comparison of clinical and laboratory features of

transthyretin VI 221 amyloidosis and immunoglobulin light chain

amyloidosis. Am Heart J 2009, 158: 607-614). [0006] Current treatment

options for FAP focus on stabilizing or decreasing the amount of

circulating amyloidogenic protein. Orthotopic liver transplantation

reduces mutant TTR levels (Holmgren et al, Biochemical effect of liver

transplantation in two Swedish patients with familial amyloidotic

polyneuropathy (FAP-met30). Clin Genet 1991, 40: 242-246), with

improved survival reported in patients with early-stage FAP, although

deposition of wild-type TTR may continue (Yazaki et al, Progressive

wild-type transthyretin deposition after liver transplantation

preferentially occurs into myocardium in FAP patients. Am J Transplant

2007, 7:235-242; Adams et al, Rapid progression of familial amyloid

polyneuropathy: a multinational natural history study Neurology 2015

Aug 25; 85(8) 675-82; Yamashita et al, Long-term survival after liver

transplantation in patients with familial amyloid polyneuropathy.

Neurology 2012, 78: 637-643; Okamoto et al., Liver

transplantation for familial amyloidotic polyneuropathy: impact on

Swedish patients’ survival. Liver Transpl 2009, 15: 1229-1235; Stangou

et al, Progressive cardiac amyloidosis following liver transplantation

for familial amyloid polyneuropathy: implications for amyloid

fibrillogenesis. Transplantation 1998, 66:229-233; Fosby et al, Liver

transplantation in the Nordic countries – An intention to treat and

post-transplant analysis from The Nordic Liver Transplant Registry

1982-2013. Scand J Gastroenterol. 2015 Jun; 50(6):797-808.

Transplantation, in press).

[0007] Tafamidis and diflunisal stabilize circulating TTR tetramers,

which can slow the rate of disease progression (Berk et al,

Repurposing diflunisal for familial amyloid polyneuropathy: a

randomized clinical trial. JAMA 2013, 310: 2658-2667; Coelho et al.,

2012; Coelho et al, Long-term effects of tafamidis for the treatment

of transthyretin familial amyloid polyneuropathy. J Neurol 2013, 260:

2802-2814; Lozeron et al, Effect on disability and safety of Tafamidis

in late onset of Met30 transthyretin familial amyloid polyneuropathy.

Eur J Neurol 2013, 20: 1539-1545). However, symptoms continue to

worsen on treatment in a large proportion of patients, highlighting

the need for new, disease-modifying treatment options for FAP.

[0008] Description of dsRNA targeting TTR can be found in, for example,

International patent application no. PCT/US2009/061381 (WO2010/048228) and

International patent application no. PCT/US2010/05531 1 (WO201

1/056883).

Summary

[0009] Described herein are methods for reducing or arresting an increase

in a Neuropathy Impairment Score (NIS) or a modified NIS (mNIS+7) in a

human subject by administering an effective amount of a transthyretin

(TTR)-inhibiting composition, wherein the effective amount reduces a

concentration of TTR protein in serum of the human subject to below 50

μg/ml or by at least 80%. Also described herein are methods for

adjusting a dosage of a TTR- inhibiting composition for treatment of

increasing NIS or Familial Amyloidotic Polyneuropathy (FAP) by

administering the TTR- inhibiting composition to a subject having the

increasing NIS or FAP, and determining a level of TTR protein in the

subject having the increasing NIS or FAP. In some embodiments, the

amount of the TTR- inhibiting composition subsequently administered to

the subject is increased if the level of TTR protein is greater than

50 μg/ml, and the amount of the TTR- inhibiting composition

subsequently administered to the subject is decreased if the level of

TTR protein is below 50 μg/ml. Also described herein are formulated

versions of a TTR inhibiting siRNA.

http://www.alnylam.com/wp-content/uploads/2017/03/Acting_Upstream_of_Today

_s_Medicines.jpg

PATENT

WO 2016203402

PAPERS

Annals of Medicine (Abingdon, United Kingdom) (2015), 47(8), 625-638.

Pharmaceutical Research (2017), 34(7), 1339-1363

Annual Review of Pharmacology and Toxicology (2017), 57, 81-105

CLIP

https://www.thepharmaletter.com/media/image/alnylam-large.jpg

 

Alnylam Announces First-Ever FDA Approval of an RNAi Therapeutic,

ONPATTRO™ (patisiran) for the Treatment of the Polyneuropathy of

Hereditary Transthyretin-Mediated Amyloidosis in Adults

Aug 10,2018

− First and Only FDA-approved Treatment Available in the United States

for this Indication –

− ONPATTRO Shown to Improve Polyneuropathy Relative to Placebo, with

Reversal of Neuropathy Impairment Compared to Baseline in Majority of

Patients –

− Improvement in Specified Measures of Quality of Life and Disease

Burden Demonstrated Across Diverse, Global Patient Population –

SOURCE

http://investors.alnylam.com/news-releases/news-release-details/alnylam-announces-first-ever-fda-approval-rnai-therapeutic?elqTrackId=5b9b83df05514e548f022d8324583ba1&elq=e50414057f3841798651d20561bbe4db&elqaid=22818&elqat=1&elqCampaignId=10597

https://endpts.com/gung-ho-alnylam-lands-historic-fda-ok-on-patisir an-revving-up-the-first-global-rollout-for-an-rnai-breakthrough/

Read Full Post »


5:00 – 5:45 PM Early Diagnosis Through Predictive Biomarkers, NonInvasive Testing

Reporter: Stephen J. Williams, Ph.D.

 

Diagnosing cancer early is often the difference between survival and death. Hear from experts regarding the new and emerging technologies that form the next generation of cancer diagnostics.

Moderator: Heather Rose, Director of Licensing, Thomas Jefferson University
Speakers:
Bonnie Anderson, Chairman and CEO, Veracyte @BonnieAndDx
Kevin Hrusovsky, Founder and Chairman, Powering Precision Health @KevinHrusovsky

Bonnie Anderson and Veracyte produces genomic tests for thyroid and other cancer diagnosis.  Kevin Hrusovksy and Precision Health uses peer reviewed evidence based medicine to affect precision medicine decision.

Bonnie: aim to get a truth of diagnosis.  Getting tumor tissue is paramount as well as properly preserved tissue.  They use deep RNA sequencing  and machine learning  in their clinically approved tests.

Kevin: Serial biospace entrepreneur.  Two diseases, cancer and neurologic, have been diseases which have been hardest to get reproducible and validated biomarkers of early disease.  He concentrates on protein biomarkers.

Heather:  FDA has recently approved drugs for early disease intervention.  However the use of biomarkers can go beyond patient stratification in clinical trials.

Kevin: 15 approved drugs for MS but the markers are scans looking for brain atrophy which is too late of an endpoint.  So we need biomarkers of early disease progression.  We can use those early biomarkers of disease progression so pharma can target those early biomarkers and or use those early biomarkers of disease progression  for endpoint

Bonnie: exciting time in the early diagnostics field. She prefers transcriptomics to DNA based methods such as WES or WGS (whole exome or whole genome sequencing).  It was critical to show data on the cost savings imparted by their transcriptomic based thryoid cancer diagnostic test for payers to consider this test eligible for reimbursement.

Kevin: There has been 20 million  CAT scans for  cancer but it is estimated 90% of these scans led to misdiagnosis. Biomarker  development  has revolutionized diagnostics in this disease area.  They have developed a breakthrough panel of ten protein biomarkers in serum which he estimates may replace 5 million mammograms.

All panelists agreed on the importance of regulatory compliance and the focus of new research should be on early detection.  In addition they believe that Dr. Gotlieb’s appointment to the FDA is a positive for the biomarker development field, as Dr. Gotlieb understands the potential and importance of early detection and prevention of disease.  Kevin also felt Dr. Gotlieb understands the importance of incorporating biomarkers as endpoints in clinical trials.  Over 750 phase 1,2, and 3 clinical trials use biomarker endpoints but the pharma companies still need to prove the biomarkers clinical relevance to the FDA.They also agreed it would be helpful to involve advocacy groups in putting more pressure on the healthcare providers and policy makers on this importance of diagnostics as a preventative measure.

In addition, the discovery and use of biomarkers as disease endpoints has led to a resurgence of Alzheimer’s disease drug development by companies which have previously given up on these type of neurodegenerative diseases.

Kevin feels proteomics offers great advantages over DNA-based diagnostics, especially in cancer such as ovarian cancer, where a high degree of specificity for a diagnostic test is required to ascertain if a woman should undergo prophylactic oophorectomy.  He suggests that a new blood-based protein biomarker panel is being developed for early detection of some forms of ovarian cancer.

Please follow on Twitter using the following #hash tags and @pharma_BI

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And at the following handles:

@pharma_BI

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Please see related articles on Live Coverage of Previous Meetings on this Open Access Journal

LIVE – Real Time – 16th Annual Cancer Research Symposium, Koch Institute, Friday, June 16, 9AM – 5PM, Kresge Auditorium, MIT

Real Time Coverage and eProceedings of Presentations on 11/16 – 11/17, 2016, The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

Tweets Impression Analytics, Re-Tweets, Tweets and Likes by @AVIVA1950 and @pharma_BI for 2018 BioIT, Boston, 5/15 – 5/17, 2018

BIO 2018! June 4-7, 2018 at Boston Convention & Exhibition Center

https://pharmaceuticalintelligence.com/press-coverage/

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Curation of selected topics and articles on Role of G-Protein Coupled Receptors in Chronic Disease as supplemental information for #TUBiol3373

Curator: Stephen J. Williams, PhD 

Below is a series of posts and articles related to the role of G protein coupled receptors (GPCR) in various chronic diseases.  This is only a cursory collection and by no means represents the complete extensive literature on pathogenesis related to G protein function or alteration thereof.  However it is important to note that, although we think of G protein signaling as rather short lived, quick, their chronic activation may lead to progression of various disease. As to whether disease onset, via GPCR, is a result of sustained signal, loss of desensitization mechanisms, or alterations of transduction systems is an area to be investigated.

From:

Molecular Pathogenesis of Progressive Lung Diseases

Author: Larry H. Bernstein, MD, FCAP

 

Chronic Obstructive Lung Disease (COPD)

Inflammatory and infectious factors are present in diseased airways that interact with G-protein coupled receptors (GPCRs), such as purinergic receptors and bradykinin (BK) receptors, to stimulate phospholipase C [PLC]. This is followed by the activation of inositol 1,4,5-trisphosphate (IP3)-dependent activation of IP3 channel receptors in the ER, which results in channel opening and release of stored Ca2+ into the cytoplasm. When ER Ca2+ stores are depleted a pathway for Ca2+ influx across the plasma membrane is activated. This has been referred to as “capacitative Ca2+ entry”, and “store-operated calcium entry” (3). In the next step PLC mediated Ca2+ i is mobilized as a result of GPCR activation by inflammatory mediators, which triggers cytokine production by Ca2+ i-dependent activation of the transcription factor nuclear factor kB (NF-kB) in airway epithelia.

 

 

 

In Alzheimer’s Disease

Important Lead in Alzheimer’s Disease Model

Larry H. Bernstein, MD, FCAP, Curator discusses findings from a research team at University of California at San Diego (UCSD) which the neuropeptide hormone corticotropin-releasing factor (CRF) as having an important role in the etiology of Alzheimer’s Disease (AD). CRF activates the CRF receptor (a G stimulatory receptor).  It was found inhibition of the CRF receptor prevented cognitive impairment in a mouse model of AD.  Furthermore researchers at the Flanders Interuniversity Institute for Biotechnology found the loss of a protein called G protein-coupled receptor 3 (GPR3) may lower the amyloid plaque aggregation, resulting in improved cognitive function.  Additionally inhibition of several G-protein coupled receptors alter amyloid precursor processing, providing a further mechanism of the role of GPCR in AD (see references in The role of G protein-coupled receptors in the pathology of Alzheimer’s disease by Amantha Thathiah and Bart De Strooper Nature Reviews Feb 2011; 12: 73-87 and read post).

 

In Cardiovascular and Thrombotic Disease

 

Adenosine Receptor Agonist Increases Plasma Homocysteine

 

and read related articles in curation on effects of hormones on the cardiovascular system at

Action of Hormones on the Circulation

 

In Cancer

A Curated History of the Science Behind the Ovarian Cancer β-Blocker Trial

 

Further curations and references of G proteins and chronic disease can be found at the Open Access journal https://pharmaceuticalintelligence.com using the search terms “GCPR” and “disease” in the Search box in the upper right of the home page.

 

 

 

 

 

 

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Untangling Dementia – Scientists used a designer compound to prevent and reverse brain damage caused by tau in mice. Miller lab, Washington University, St. Louis

Reporter: Aviva Lev-Ari, PhD, RN

Designer compound may untangle damage leading to some dementias

NIH-funded preclinical study suggests a possible treatment for Alzheimer’s disease and other neurodegenerative disorders.

In a study of mice and monkeys, National Institutes of Health funded researchers showed that they could prevent and reverse some of the brain injury caused by the toxic form of a protein called tau. The results, published in Science Translational Medicine, suggest that the study of compounds, called tau antisense oligonucleotides, that are genetically engineered to block a cell’s assembly line production of tau, might be pursued as an effective treatment for a variety of disorders.

Cells throughout the body normally manufacture tau proteins. In several disorders, toxic forms of tau clump together inside dying brain cells and form neurofibrillary tangles, including Alzheimer’s disease, tau-associated frontotemporal dementia, chronic traumatic encephalopathy and progressive supranuclear palsy. Currently there are no effective treatments for combating toxic tau.

“This compound may literally help untangle the brain damage caused by tau,” said Timothy Miller, M.D., Ph.D., the David Clayson Professor of Neurology at Washington University, St. Louis, and the study’s senior author.

Antisense oligonucleotides are short sequences of DNA or RNA programmed to turn genes on or off. Led by Sarah L. DeVos, a graduate student in Dr. Miller’s lab, the researchers tested sequences designed to turn tau genes off in mice that are genetically engineered to produce abnormally high levels of a mutant form of the human protein. Tau clusters begin to appear in the brains of 6-month-old mice and accumulate with age. The mice develop neurologic problems and die earlier than control mice.

Injections of the compound into the fluid filled spaces of the mice brains prevented tau clustering in 6-9 month old mice and appeared to reverse clustering in older mice. The compound also caused older mice to live longer and have healthier brains than mice that received a placebo. In addition, the compound prevented the older mice from losing their ability to build nests.

SOURCE

https://www.nih.gov/news-events/news-releases/designer-compound-may-untangle-damage-leading-some-dementias

 

Tau reduction prevents neuronal loss and reverses pathological tau deposition and seeding in mice with tauopathy

Science Translational Medicine  25 Jan 2017:
Vol. 9, Issue 374,
DOI: 10.1126/scitranslmed.aag0481

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Reversal of Alzheimer Disease in Fruit Flies

Curator: Larry H. Bernstein, MD, FCAP

 

 

Reversal of AD in fruit flies

Transatlantic team reverses Alzheimer’s, Parkinson’s symptoms in fruit flies

by Amirah Al Idrus | Apr 26, 2016

http://www.fiercebiotech.com/research/transatlantic-team-reverses-alzheimer-s-parkinson-s-symptoms-fruit-flies

Scientists from the University of Leicester and the University of Maryland have reversed Alzheimer’s and Parkinson’s symptoms by inhibiting an enzyme in fruit fly models, highlighting a new avenue to treat neurodegenerative diseases.

Maryland’s Robert Schwarcz and Leicester’s Flaviano Giorgini studied the amino acid tryptophan, which degrades in the body into several metabolites that have different effects on the nervous system. These include 3-hydroxykynurenine (3-HK), which can damage the nervous system, and kynurenic acid (KYNA), which can prevent nerve degeneration. The relative abundance of these two compounds in the brain could be critical in Parkinson’s, Alzheimer’s and Huntington’s disease, the University of Maryland said in a statement.

3-HK and KYNA  exist in a balance between “good” and “bad” metabolites in the body. In neurodegenerative disease, the balance shifts toward the “bad,” Giorgini said in a statement. The researchers shifted the balance back toward “good” by giving genetically modified fruit flies a chemical that selectively inhibits the enzyme TDO, which controls the relationship between 3-HK and KYNA. This increased levels of the “protective” KYNA, and improved movement and lengthened life span in fruit flies genetically modified to model neurodegenerative disease.

Giorgini’s team at Leicester has previously used genetic approaches to inhibit TDO and another enzyme, KMO. The treatment lowered the levels of toxic tryptophan metabolites and reduced neuron loss in fruit fly models of Huntington’s disease.

It is estimated that 5 million Americans have Alzheimer’s disease and as many as 1 million have Parkinson’s. Current treatments may help to control symptoms but do not halt or delay disease progression. “Our hope is that by improving our knowledge of how these nerve cells become sick and die in the brain, we can help devise ways to interfere with these processes, and thereby either delay disease onset or prevent disease altogether,” Giorgini said in the Leicester statement. Giorgini’s next step will be to validate the work in mammalian models.

Meanwhile, a UC San Diego team recently spotlighted the dendritic spines of neurons as a possible target in Alzheimer’s. And The Wall Street Journalreported this week that seniors are clamoring to participate in a clinical trial to see if the diabetes drug metformin can stave off the diseases that come with aging, including cognitive decline.

– here’s a statement from the University of Maryland
– and here’s the University of Leicester’s statement
– read the study abstract

 

Tryptophan-2,3-dioxygenase (TDO) inhibition ameliorates neurodegeneration by modulation of kynurenine pathway metabolites

Carlo BredaaKorrapati V. SathyasaikumarbShama Sograte IdrissiaFrancesca M. Notarangelob,
….., Robert Schwarczb, and Flaviano Giorginia,1
http://www.pnas.org/content/early/2016/04/21/1604453113.abstract

Significance

Neurodegenerative diseases such as Alzheimer’s (AD), Parkinson’s (PD), and Huntington’s (HD) present a significant and increasing burden on society. Perturbations in the kynurenine pathway (KP) of tryptophan degradation have been linked to the pathogenesis of these disorders, and thus manipulation of this pathway may have therapeutic relevance. Here we show that genetic inhibition of two KP enzymes—kynurenine-3-monooxygenase and tryptophan-2,3-dioxygenase (TDO)—improved neurodegeneration and other disease symptoms in fruit fly models of AD, PD, and HD, and that alterations in levels of neuroactive KP metabolites likely underlie the beneficial effects. Furthermore, we find that inhibition of TDO using a drug-like compound reverses several disease phenotypes, underscoring the therapeutic promise of targeting this pathway in neurodegenerative disease.

Abstract

Metabolites of the kynurenine pathway (KP) of tryptophan (TRP) degradation have been closely linked to the pathogenesis of several neurodegenerative disorders. Recent work has highlighted the therapeutic potential of inhibiting two critical regulatory enzymes in this pathway—kynurenine-3-monooxygenase (KMO) and tryptophan-2,3-dioxygenase (TDO). Much evidence indicates that the efficacy of KMO inhibition arises from normalizing an imbalance between neurotoxic [3-hydroxykynurenine (3-HK); quinolinic acid (QUIN)] and neuroprotective [kynurenic acid (KYNA)] KP metabolites. However, it is not clear if TDO inhibition is protective via a similar mechanism or if this is instead due to increased levels of TRP—the substrate of TDO. Here, we find that increased levels of KYNA relative to 3-HK are likely central to the protection conferred by TDO inhibition in a fruit fly model of Huntington’s disease and that TRP treatment strongly reduces neurodegeneration by shifting KP flux toward KYNA synthesis. In fly models of Alzheimer’s and Parkinson’s disease, we provide genetic evidence that inhibition of TDO or KMO improves locomotor performance and ameliorates shortened life span, as well as reducing neurodegeneration in Alzheimer’s model flies. Critically, we find that treatment with a chemical TDO inhibitor is robustly protective in these models. Consequently, our work strongly supports targeting of the KP as a potential treatment strategy for several major neurodegenerative disorders and suggests that alterations in the levels of neuroactive KP metabolites could underlie several therapeutic benefits.

neurodegeneration, KMO, TDO, Parkinson’s disease, Alzheimer’s disease

 

The kynurenine pathway (KP), the major catabolic route of tryptophan (TRP) metabolism in mammals (Fig. 1), has been closely linked to the pathogenesis of several brain disorders (1). This pathway contains several neuroactive metabolites, including 3-hydroxykynurenine (3-HK), quinolinic acid (QUIN) and kynurenic acid (KYNA) (2). QUIN is a well-characterized endogenous neurotoxin that specifically activates N-methyl-D-aspartate (NMDA) receptors, thereby inducing excitotoxicity (34). The metabolites 3-HK and QUIN are also neurotoxic via the generation of free radicals and oxidative stress (56). Conversely, KYNA—synthesized by kynurenine aminotransferases (KATs)—is neuroprotective through its antioxidant properties and antagonism of both the α7 nicotinic acetylcholine receptor and the glycine coagonist site of the NMDA receptor (713). Levels of these metabolites are regulated at two critical points in the KP: (i) the initial, rate-limiting conversion of TRP into N-formylkynurenine by either tryptophan-2,3-dioxygenase (TDO) or indoleamine-2,3-dioxygenase 1 and 2 (IDO1 and IDO2); and (ii) synthesis of 3-HK from kynurenine by the flavoprotein kynurenine-3-monoxygenase (KMO) (1).

 

 

Fig. 1.

Consequences of KP manipulation. KP metabolites and enzymatic steps are indicated in black, whereas the key KP enzymes TDO, KMO, and KATs are indicated in purple. The metabolites 3-HK and QUIN are neurotoxic (as indicated by red arrows), whereas KYNA and TRP are neuroprotective (as indicated by green arrows). Inhibition of TDO results in increased TRP levels, and either TDO or KMO inhibition leads to a reduction in the 3-HK/KYNA ratio (highlighted in blue). The enzyme 3-hydroxyanthranilic acid dioxygenase is not present in flies, and thus QUIN is not synthesized.

Alterations in levels of the KP metabolites have been observed in a broad range of brain disorders, including both neurodegenerative and psychiatric conditions (14). In neurodegenerative diseases such as Huntington’s (HD), Parkinson’s (PD), and Alzheimer’s (AD), a shift toward increased synthesis of the neurotoxic metabolites QUIN and 3-HK relative to KYNA may contribute to disease (1). Indeed, in patients with HD and HD model mice, 3-HK and QUIN levels are increased in the neostriatum and cortex (1516). Moreover, KYNA levels are reduced in the striatum of patients with HD (17). Several studies have also found perturbation in KP metabolites in the blood and cerebrospinal fluid of patients with AD, with decreased levels of KYNA correlating with reduced cognitive performance (1819). Similarly, in the basal ganglia of patients with PD, a reduction in KYNA levels combined with increased 3-HK has been observed (2021).

Drosophila melanogaster has provided a useful model for interrogation of the KP in both normal physiology and in neurodegenerative disease (2223). In fruit flies, TDO and KMO are encoded by vermillion (v) andcinnabar (cn), respectively, and both are implicated in Drosophila eye color pigmentation and brain plasticity (2425). In flies, TDO is the sole enzyme that catalyzes the initial step of the KP, as IDO1 and IDO2 are not present (Fig. 1), and so provides a distinctive model for examining the role of this critical step in the pathway. Moreover, we have previously found that downregulating cn and v gene expression significantly reduces neurodegeneration in flies expressing a mutant huntingtin (HTT) fragment—the central causative insult underlying HD (22). We also observed that pharmacological manipulations that reduced the 3-HK/KYNA ratio were always associated with neuroprotection. Notably, reintroduction of physiological levels of 3-HK in HD flies that lacked this metabolite due to KMO inhibition was sufficient to abolish neuroprotection (22). Furthermore, in a Caenorhabditis elegans model of PD, genetic down-regulation of TDO ameliorates α-synuclein (aSyn) toxicity (26). This effect appeared to be independent of changes in the levels of serotonin or KP metabolites but was correlated with increased TRP levels. Supplementing worms with TRP also suppressed aSyn-dependent phenotypes (26). The present study was designed to further define the mechanism(s) that underlies the neuroprotection conferred by TRP treatment and TDO inhibition and to extend our analyses of the neuroprotective potential of the KP to fruit fly models of AD and PD.

 

Discussion

Impairments in KP metabolism have been linked to several neurodegenerative disorders, and in particular to the pathogenesis of HD (37). Notably, increased levels of 3-HK and QUIN have been measured in the neostriatum and cortex of patients with early stage HD (15), and these changes are associated with an up-regulation of IDO1 transcription (38) and a reduction in the activity of KAT, which is critical for KYNA synthesis (17). These data in patients with HD are supported by observations in HD mice, which show increased cerebral KMO activity (39). We previously found that either genetic or pharmacological inhibition of KMO is protective in HD flies and leads to a corresponding increase in KYNA levels relative to 3-HK (22). Furthermore, we reported that KYNA treatment reduced neurodegeneration in these flies. Here, we have extended this work by generating transgenic flies that overexpress hKAT and thereby synthesize ∼20-fold more KYNA than control flies. This increased formation of KYNA reduced neurodegeneration and eclosion defects in HD model flies. Furthermore, KMO inhibition by RNAi revealed beneficial effects in several behavioral and disease-relevant outcome measures, including larval crawling, longevity, climbing, and rhabdomere degeneration, in AD and PD model flies. These results strongly support the notion that KMO inhibition has relevance as a treatment strategy in a broad range of neurodegenerative diseases. In addition, these data also suggest that the design of small molecules capable of increasing KAT activity could have therapeutic relevance for neurodegenerative disorders.

The present results, demonstrating that both genetic and pharmacological inhibition of TDO provides robust neuroprotection in fly models of AD and PD, also confirmed and extended the results of our previous study, which had identified TDO as a candidate drug target in HD flies (22). These protective effects are associated with a decrease in the 3-HK/KYNA ratio, i.e., a shift toward increased KYNA synthesis. Work inC. elegans has revealed that TDO inhibition is also protective in models of proteotoxicity, although amelioration of the phenotypes occurred independently of changes in the levels of KP metabolites and was instead associated with elevated TRP levels (26). Although the underlying mechanism remained unclear, the favorable effects of high TRP levels in the nematode were substantiated by the fact that TRP treatment conferred robust protection from disease-related phenotypes (Fig. 1). In the present study, too, TRP supplementation of the diet was effective, ameliorating rhabdomere degeneration and eclosion defects in HD flies. However, TRP feeding was also associated with a reduction in the 3-HK/KYNA ratio, suggesting that the protective effects of the amino acid may be linked to an increase in the production of the neuroprotective metabolite KYNA (Fig. 1). Indeed, partial inhibition of KYNA synthesis in TDO-deficient flies proved sufficient to completely reverse neuroprotection. In addition, restoration of physiological 3-HK levels in TDO-deficient HD flies did not reverse neuroprotection, in contrast to KMO-deficient flies (22). In primary neurons, 3-HK toxicity is dependent upon its uptake via neutral amino acid transporters, and coapplication of TRP can block this toxicity by competing for the same transporters (6). Thus, it is possible that the vast excess of TRP observed in the heads of HTT93Q v−/− flies (approximately eightfold versus controls) competes with 3-HK for rhabdomere uptake, thereby requiring hyperphysiological levels of 3-HK to reverse TDO-dependent neuroprotection. A similar mechanism may also contribute to the neuroprotection observed with TRP treatment in general. Herein, we have also found that RNAi knockdown of either cn or v does not increase TRP levels, and thus the neuroprotection observed in the AD and PD flies strongly correlates with a decrease in the 3-HK/KYNA ratio. The mechanism causing TRP treatment to favor KYNA synthesis over the formation of 3-HK in Drosophila, as well as the unexpected qualitative differences in the effects of TDO inhibition and TRP administration on KP metabolism between fruit flies and nematodes, clearly requires further investigation.

Interestingly, we found that QUIN—which is not normally synthesized in fruit flies (30)—potentiated neurodegeneration in HD flies, and reversed the protective effects of KMO inhibition. As the same QUIN treatment did not cause neuron loss in wild-type flies, mutant HTT may potentiate vulnerability by enhancing NMDA receptor function (4041) and/or by increasing susceptibility to toxic free radicals (42), i.e., by augmenting the two major mechanisms known to be involved in QUIN-induced neurotoxicity (43). If verified in mammals, a reduction in brain QUIN levels—along with a decrease in 3-HK levels—relative to KYNA could therefore be especially promising in the treatment of HD (44). Our observation of increased levels of QUIN in HTT93Q versus WT flies is enigmatic, but may be due to altered feeding behavior, increased permeability of the blood–brain barrier (4546), or differences in KP metabolism, and would be interesting to explore in future studies.

In conclusion, the present set of experiments further validates the hypothesis that KP metabolism is causally linked to neuronal viability and that modulation of the KP constitutes a promising therapeutic strategy for a variety of major neurodegenerative disorders. Notably, we provide the first genetic evidence to our knowledge that KMO inhibition is protective in animal models of PD and AD and that pharmacological targeting of TDO is also neuroprotective. We have clarified the mechanism underlying the protective effects of TDO inhibition, which will stimulate efforts to target this step of the KP in neurodegenerative disease. These results, together with supportive studies in flies (47) and rodents (48), raise the possibility that inhibition of TDO and KMO—or combinatorial treatment—may offer therapeutic advantages. The availability of new TDO inhibitors (4950), and access to the crystal structures of both TDO (51) and KMO (52), should allow further testing of these hypotheses in the near future.

 

 

 

 

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