Folate regulation of axonal regeneration in the rodent central nervous system through DNA methylation.

The folate pathway plays a crucial role in the regeneration and repair of the adult CNS after injury. Here, we have shown in rodents that such repair occurs at least in part through DNA methylation. In animals with combined spinal cord and sciatic nerve injury, folate-mediated CNS axon regeneration was found to depend on injury-related induction of the high-affinity folate receptor 1 (Folr1). The activity of folate was dependent on its activation by the enzyme dihydrofolate reductase (Dhfr) and a functional methylation cycle. The effect of folate on the regeneration of afferent spinal neurons was biphasic and dose dependent and correlated closely over its dose range with global and gene-specific DNA methylation and with expression of both the folate receptor Folr1 and the de novo DNA methyltransferases. These data implicate an epigenetic mechanism in CNS repair. Folic acid and possibly other nontoxic dietary methyl donors may therefore be useful in clinical interventions to promote brain and spinal cord healing. If indeed the benefit of folate is mediated by epigenetic mechanisms that promote endogenous axonal regeneration, this provides possible avenues for new pharmacologic approaches to treating CNS injuries.

Improved delineation of short cortical association fibers and gray/white matter boundary using whole-brain three-dimensional diffusion tensor imaging at submillimeter spatial resolution.

Recent emergence of human connectome imaging has led to a high demand on angular and spatial resolutions for diffusion magnetic resonance imaging (MRI). While there have been significant growths in high angular resolution diffusion imaging, the improvement in spatial resolution is still limited due to a number of technical challenges, such as the low signal-to-noise ratio and high motion artifacts. As a result, the benefit of a high spatial resolution in the whole-brain connectome imaging has not been fully evaluated in vivo. In this brief report, the impact of spatial resolution was assessed in a newly acquired whole-brain three-dimensional diffusion tensor imaging data set with an isotropic spatial resolution of 0.85 mm. It was found that the delineation of short cortical association fibers is drastically improved as well as the definition of fiber pathway endings into the gray/white matter boundary-both of which will help construct a more accurate structural map of the human brain connectome.

Assessment of the Spatial and Temporal Distribution of Functional Connectivity in Resting-State BOLD fMRI

Recent research into resting-state functional magnetic resonance imaging (fMRI) has shown that the brain is very active during rest. This thesis work utilizes blood oxygenation level dependent (BOLD) signals to investigate the spatial and temporal functional network information found within resting-state data, and aims to investigate the feasibility of extracting functional connectivity networks using different methods as well as the dynamic variability within some of the methods. Furthermore, this work looks into producing valid networks using a sparsely-sampled sub-set of the original data.

In this work we utilize four main methods: independent component analysis (ICA), principal component analysis (PCA), correlation, and a point-processing technique. Each method comes with unique assumptions, as well as strengths and limitations into exploring how the resting state components interact in space and time.

Correlation is perhaps the simplest technique. Using this technique, resting-state patterns can be identified based on how similar the time profile is to a seed region’s time profile. However, this method requires a seed region and can only identify one resting state network at a time. This simple correlation technique is able to reproduce the resting state network using subject data from one subject’s scan session as well as with 16 subjects.

Independent component analysis, the second technique, has established software programs that can be used to implement this technique. ICA can extract multiple components from a data set in a single analysis. The disadvantage is that the resting state networks it produces are all independent of each other, making the assumption that the spatial pattern of functional connectivity is the same across all the time points. ICA is successfully able to reproduce resting state connectivity patterns for both one subject and a 16 subject concatenated data set.

Using principal component analysis, the dimensionality of the data is compressed to find the directions in which the variance of the data is most significant. This method utilizes the same basic matrix math as ICA with a few important differences that will be outlined later in this text. Using this method, sometimes different functional connectivity patterns are identifiable but with a large amount of noise and variability.

To begin to investigate the dynamics of the functional connectivity, the correlation technique is used to compare the first and second halves of a scan session. Minor differences are discernable between the correlation results of the scan session halves. Further, a sliding window technique is implemented to study the correlation coefficients through different sizes of correlation windows throughout time. From this technique it is apparent that the correlation level with the seed region is not static throughout the scan length.

The last method introduced, a point processing method, is one of the more novel techniques because it does not require analysis of the continuous time points. Here, network information is extracted based on brief occurrences of high or low amplitude signals within a seed region. Because point processing utilizes less time points from the data, the statistical power of the results is lower. There are also larger variations in DMN patterns between subjects. In addition to boosted computational efficiency, the benefit of using a point-process method is that the patterns produced for different seed regions do not have to be independent of one another.

This work compares four unique methods of identifying functional connectivity patterns. ICA is a technique that is currently used by many scientists studying functional connectivity patterns. The PCA technique is not optimal for the level of noise and the distribution of the data sets. The correlation technique is simple and obtains good results, however a seed region is needed and the method assumes that the DMN regions is correlated throughout the entire scan. Looking at the more dynamic aspects of correlation changing patterns of correlation were evident. The last point-processing method produces a promising results of identifying functional connectivity networks using only low and high amplitude BOLD signals.

Viral Delivery of the Fat-1 Gene to Treat Post-Traumatic Arthritis with Diet-Induced Obesity

Post-traumatic arthritis (PTA) is arthritis that develops following joint injury, including meniscus and ligament tears. Current treatments for PTA range from over-the-counter medication to knee replacement; however, in the presence of obesity, the levels of pro-inflammatory cytokines, such as interleukin-1 (IL-1) and tumor necrosis factor alpha (TNF-α,) are more elevated than in non-obese individuals. The role of fatty acids, obesity, and PTA has been examined, with omega-3 fatty acids showing promise as an anti-inflammatory after injury due to its ability to suppress IL-1 and TNF-α. Due to the difficulty in switching patients’ diets, an alternative solution to increasing omega-3 levels needs to be developed. The Fat-1 enzyme, an omega-3 desaturase that has the ability to convert omega-6 to omega-3 fatty acids, may be a good target for increasing the omega-3 levels in the body.

In the first study, we examined whether Fat-1 transgenic mice on a high-fat diet would exhibit lower levels of PTA degeneration following the destabilization of the medial meniscus (DMM). Both male and female Fat-1 and wild-type (WT) littermates were put on either a control diet (10% fat) or an omega-6 rich high-fat diet (60% fat) and underwent DMM surgery. Arthritic changes were examined 12 weeks post-surgery. Fat-1 mice on both the control and high-fat diet showed protection from PTA-related degeneration, while WT mice showed severe arthritic changes. These findings suggest that the omega-6/omega-3 ratio plays an important role in reducing PTA following injury, and demonstrates the potential therapeutic benefit of the Fat-1 enzyme in preventing PTA in both normal and obese patients following acute injury.

Following this, we needed to establish a translatable delivery mechanism for getting the Fat-1 enzyme, which is not present in mammalian cells, into patients. In the second study, we examined whether anti-inflammatory gene delivery of the Fat-1 enzyme would prevent PTA following DMM surgery. In vitro testing of both lentivirus (LV) and adeno-associated virus (AAV) was completed to confirm functionality and conformation of the Fat-1 enzyme after transduction. Male WT mice were placed on an omega-6 rich high-fat diet (60% fat) and underwent DMM surgery; either local or systemic AAV injections of the Fat-1 enzyme or Luciferase, a vector control, were given immediately following surgery. 12 weeks post-surgery, arthritic changes were assessed. The systemic administration of the Fat-1 enzyme showed protection from synovial inflammation and osteophyte formation, while administration of Luciferase did not confer protection. These findings suggest the utility of gene therapy to deliver the Fat-1 enzyme, which has potential as a therapeutic for injured obese patients for the prevention of PTA.