Factor analysis reveals differences in brain metabolism in macaques with SIV/AIDS and those with...

Factor analysis reveals differences in brain metabolism inmacaques with SIV/AIDS and those with SIV-induced encephalitis

Margaret R. Lentz1, Vallent Lee1, Susan V. Westmoreland2, Eva-Maria Ratai1, Elkan F.Halpern1, and R. Gilberto González1,*

1Department of Neuroradiology/A. A. Martinos Center for Biomedical Imaging, Massachusetts GeneralHospital, Charlestown, MA 02129, USA

2New England Primate Research Center, Southborough, MA 01772, USA

AbstractMRS has often been used to study metabolic processes in the HIV-infected brain. However, it remainsunclear how changes in individual metabolites are related to one another in this context of virus-induced central nervous system dysfunction. We used factor analysis (FA) to identify patterns ofmetabolite distributions from an MRS study of healthy macaques and those infected with simianimmunodeficiency virus (SIV) which were moribund with AIDS. FA summarized the correlationsfrom nine metabolites into three main factors. Factor 3 identified patterns that discern healthy animalsfrom those with SIV/AIDS. Factor 2 was able to differentiate between animals that had encephalitisand those moribund with AIDS but lacking encephalitis. Specifically, Factor 2 was able to distinguishanimals with moderate to severe encephalitis from animals with mild or no encephalitis as well asuninfected controls. FA not only confirmed the involvement of neuronal metabolites (N-acetylaspartate and glutamate) in disease severity, but also detected changes in creatine and myo-inositol that have not been observed in the SIV macaque model previously. These results suggestthat the divergent pathways of N-acetylaspartate and creatine in this disease may enable thecommonly reported ratio N-acetylaspartate/creatine to be a more sensitive marker of disease severity.

Keywordshuman immunodeficiency virus (HIV); simian immunodeficiency virus (SIV); MRS; N-acetylaspartate; creatine; encephalitis; frontal cortex; factor analysis

INTRODUCTIONInfection with human immunodeficiency virus (HIV) can often lead to encephalitis anddementia induced by central nervous system penetration by the virus and not as an opportunisticinfection (1,2). Proposed mechanisms for this injury include neurotoxic factors andinflammatory cytokines that can produce neuronal injury and gliosis (3). MRS has been usedto investigate HIV-induced brain disease, which has documented metabolic changes in patientsat various stages of infection (4-7). Such studies have commonly reported decreases in theneuronal marker N-acetylaspartate (NAA) and increases in myo-inositol (MI) and/or choline-containing compounds (Cho). These metabolites are often reported as ratios with respect tototal creatine (Cr), which is used as an internal standard because stable energy metabolism is

*Correspondence to: R. G. González, Neuroradiology Division, GRB 285, Massachusetts General Hospital, 55 Fruit Street, Boston, MA02114−2696, USA. E-mail: [email protected].

NIH Public AccessAuthor ManuscriptNMR Biomed. Author manuscript; available in PMC 2008 October 6.

Published in final edited form as:NMR Biomed. 2008 October ; 21(8): 878–887. doi:10.1002/nbm.1276.

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assumed. However, the belief that creatine is unvarying in neurodegenerative disease isdebated, and changes in this marker have been reported in neuroAIDS (8).

In the simian immunodeficiency virus (SIV) macaque model, NAA/Cr changes have beenreported and have, in some instances, been found to be reversible at different stages of SIVinfection (9-12). However, significant Cho/ Cr and MI/Cr changes have only been observedduring acute infection. Recently, we reported that NAA/Cr could serve as a biomarker thatindicates the severity of encephalitis in SIV-infected macaques (13). We also noted changesin other neurotransmitter ratios, such as glutamate (Glu) and g-aminobutyric acid (GABA)relative to Cr, but these were found to have little bearing on the severity of encephalitis whenanalyzed individually, even though both correlated very well with NAA.

Although MRS allows quantification of several brain metabolites, it remains unclear how thevarious changes in these markers relate to one another in the context of HIV/SIVneuropathogenesis. Factor analysis (FA) is a tool for uncovering relationships in a multivariatedataset. This statistical technique (of which principal components analysis is the first step)summarizes the variance in the data by generating linear combinations of variables, reducingthe dimensionality down to a few orthogonal factors, and thus eliminating “noise” caused byother irrelevant variables measured. This has been particularly helpful in research that utilizesMRS, where the identification of predominant metabolic profiles has been pivotal to the fieldof metabonomics (14-17). As more metabolites become quantifiable from in vivo spectra, FAcould be effective in reducing error from individual tests as well as uncovering broader patternsin the metabolic processes underlying different stages in the pathogenesis of neuroAIDS. Asyet, this method has seldom been applied to HIV MRS studies. AIDS-relatedleukoencephalopathy has been compared with profiles of other neurological diseases (18,19),and another study examined pediatric cases of HIV-related encephalopathy (20). Morerecently, the technique has been applied to meta-bolite ratios of HIV-infected subjects acrossthree other brain regions to distinguish between subjects with AIDS dementia complex andthose who were asymptomatic (21), and a similar study has correlated MRS-derived factorswith monocyte chemoattractant protein-1 (22).

Our study uses a multivariate factor analysis (FA) approach to examine distributions ofmetabolites from a MRS study of SIV-induced encephalitis (SIVE) in rhesus macaques foundto have terminal AIDS (13). The purpose of this analysis was to identify metabolite patternsassociated with SIV/AIDS and severity of virus-induced encephalitis. By moving beyondanalysis of individual metabolites, this method may (i) improve our understanding of how MRSmarkers of metabolic processes are related in this disease and (ii) help to resolve apparentinconsistencies in the literature that have arisen from traditional univariate methods involvingratios with respect to Cr or H2O.

EXPERIMENTALThis is a further analysis of data from a recent study on the predictive power of NAA/Cr inSIVE (13). Detailed descriptions of the animals in this study have been previously reported(13). Briefly, 23 rhesus macaques (Macaca mulatta) were infected with SIV (SIVmac251 or239) and euthanized when moribund with AIDS. Their brains were classified as non-encephalitic (n = 6) or encephalitic (n = 17) on the basis of the presence of multinucleated giantcells (the hallmark of HIV/SIV encephalitis) in many regions of the brain, including the frontalcortex. These encephalitic animals were further classified as having mild (n = 6), moderate (n= 4), or severe (n = 7) encephalitis on the basis of the quantity of multinucleated giant cellsobserved. Six non-infected macaques were sacrificed as controls. The age of the animals rangedfrom 1.7 to 10.3 years. Previous results indicated no age effect between cohorts (13). Nineanimals were female and 20 were male. The study was approved by both Massachusetts General

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NMR Biomed. Author manuscript; available in PMC 2008 October 6.

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Hospital's Subcommittee on Research Animal Care and the Institutional Animal Care and UseCommittee of Harvard University.

Frozen frontal cortex tissues from these 29 animals were extracted with chloroform/methanol,as previously described (13). Extracted metabolites were analyzed with high-resolution 1HMRS on a Bruker Avance 600 MHz 54 mm vertical-bore spectrometer using the XWIN-NMR3.5 software package (Bruker Instruments Inc., Billerica, MA, USA). A CP TXI 5 mm H-C/N-D Z-gradient cryo-probe was used for the one-pulse experiments with spectral acquisitionparameters including a 90° pulse width of 9 μs, 20 s recycle time, 7.2 kHz spectral width, 32000complex points, and 64 scans that were averaged (13). Spectral peaks were referenced to TMSand fitted using PERCH NMR software version 1/2005 (PERCH Solutions Ltd, Kuopio,Finland). The metabolites included NAA, Cr, total choline-containing compounds (Cho; 3.19−3.22 ppm), MI, GABA, Glu, glutamine (Gln), N-acetylaspartylglutamate (NAAG), andglycine (Gly) (Fig. 1). As NAA breaks down to acetate and aspartate in even the most carefullyharvested and stored samples, NAA reported herein represents the sum of the acetate resonanceat 1.91 ppm to the NAA resonance at 2.01 ppm (23).

The relative concentration of each metabolite was calculated by dividing its area by the totalarea of all metabolites of interest (Met/Σ), and patterns of metabolite concentrations wereexamined by FA. This method differs from another approach used in metabonomicspectroscopy studies in which the variables examined with FA are regions of adjacent spectralpoints grouped into ‘bins’ or ‘buckets,’ and thus FA produces linear combinations of spectralregions, rather than combinations of metabolites (24). Here, deconvolution of peaks formetabolite quantitation results in factors that represent linear combinations of metaboliteconcentrations while obviating the need for inter-sample alignment and reducing thecomplications from overlapping signals.

Factor analysisFA is an exploratory method of data reduction that explains many variables with just a fewcomponents or factors by grouping correlated variables together. First, the principalcomponents are identified. The variables are standardized (subtract the mean and divide by thestandard deviation for each variable in each case), and new orthogonal axes are successivelyassigned along the directions of greatest variation. These are the eigenvectors of the correlationmatrix. Most of the variation in the sample is captured in the first few components. Accordingto the Kaiser criterion, only the components with eigenvalues greater than 1 are kept, as thesecontain a greater proportion of the variance than any of the original variables individually(25). The remaining variation in the dataset is considered to be noise. When the data are plottedusing these new axes, in the principal components space correlated variables will clustertogether.

Varimax rotation was applied to these components to aid their interpretation, generating rotatedfactors that align more closely with the directions of these clusters. The simple structureobtained from this orthogonal transformation aids in the interpretation of factors by makingclusters of correlated variables more prominent, while keeping the factors uncorrelated witheach other (25). This resulting factor loading matrix is reported, from which investigatorsattempt to infer the latent structure (referred to as loadings) of patterns or causes underlyingthe observable variables. In addition, for each case, values for each factor can be calculatedfrom the original variables using a scoring matrix. The scoring matrix is obtained bymultiplying the loading matrix by the inverse of the correlation matrix. The resulting scorescan be used for further analysis of variance (ANOVA) across groups or for correlations withadditional measures.

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Analysis of disease severityFor this study, each animal was assigned a score for each factor based on the respective loadingsof its nine metabolite concentrations. The association between the resulting components andthe severity of encephalitis was assessed by ANOVA. If the ANOVA was significant, specificdifferences between disease classifications were isolated using two-tailed least squares meanst tests. Statistical tests were performed using JMP 5 software (SAS Institute, Cary, NC, USA).

RESULTSFA identified three main factors that jointly accounted for 67% of the variance in the data(Table 1). Loadings for each metabolite found to have a strong (0.6+) or moderate (0.4−0.6)involvement appear in bold in Table 1, and these metabolites are noted in Fig. 1. Each animalwas assigned a score for each factor based on its loadings (Table 2). The scores for Factors 2and 3 were significantly different across cohorts (ANOVA: P = 0.005, P < 0.05, respectively),whereas scores for Factor 1 were not (ANOVA: P = 0.82).

Analysis of healthy and SIV-infected macaquesScores for Factor 3 were able to discern between animals that had SIV/AIDS and those thatdid not. Lower scores were associated with SIV infection (Fig. 2). Factor 3 was able todistinguish control animals from infected animals with SIVE (P < 0.03), as well as from infectedanimals without encephalitis (P = 0.02). However, no distinction between the two SIV-infectedgroups could be found using this factor (P = 0.55).

Analysis of encephalitis and SIVE severity among macaquesFactor 2 was able to discriminate animals with SIVE from uninfected controls (P = 0.002), andfrom animals moribund with AIDS without SIVE (P = 0.04) (Fig. 3, left). This factor was,however, unable to discern between control animals and infected animals that lack encephalitis(P = 0.32). Further analysis indicated that Factor 2 could distinguish between diseaseclassifications (ANOVA: P < 0.002), with lower scores associated with encephalitis severity(Fig. 3, right). Factor 2 was able to differentiate animals with severe encephalitis from controlanimals (P < 0.0003), as well as from those found moribund with AIDS but lacking signs ofencephalitis (P < 0.005). Factor 2 was also able to distinguish animals with moderateencephalitis from control animals (P = 0.004), as well as from animals found to be moribundwith AIDS without encephalitis (P = 0.04). Factor 2 was significantly different betweenseverely and mildly encephalitic animals (P = 0.01), but was not significantly different betweenmoderately and mildly encephalitic animals (P < 0.08). Loadings from Factor 2 represent alinear combination of changes in both primarily neuronal (NAA/Σ, Glu/Σ) and primarily glial(Cr/Σ, MI/Σ) metabolites during SIV infection. Graphical representations of the mean valuesof these four metabolites for each cohort are shown in order to clarify the ways in which thesemetabolites are changing (Fig. 4). This figure illustrates that the changes in Factor 2 arise fromthe decreases in NAA/Σ and Glu/Σ and increases in Cr/Σ and MI/Σ that occur with diseaseseverity.

DISCUSSIONCurrently, there are many ways to evaluate MRS data, including ratios with respect to Cr(13,21), H2O (sometimes referred to as ‘absolute concentrations’) (7,22), or the total area ofthe spectrum (19,20), as well as the use of different statistical analyses. Various methods havebeen tried in human and macaque studies, which have resulted in some apparent contradictions.It is unclear whether these occasional inconsistencies are the result of differences inmeasurement or data analysis. Here, we present FA as an alternative means of examiningspectroscopy data in HIV/SIV studies and have obtained results that shed new light on some

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inconsistencies reported from traditional univariate methods involving ratios with respect toCr or H2O.

Application of FAFA seeks to find the latent structure of patterns underlying the observable variables. Inpsychological studies that involve surveys, FA may be used to identify the opinions andattitudes (unobservable factors) that generate the answers given in a questionnaire (observablevariables). In our study, we seek relationships (unobservable factors) between metabolites thatcan explain the changes in individual metabolite concentrations (observable variables). Theloading matrix extracts the factors from the original variables. Just as the answers to a varietyof questions may all stem from a few basic beliefs held by an individual, so the changes inthese metabolite concentrations may derive from a few core mechanisms or physiologicalresponses. The scoring matrix gives the reverse conversion, combining observable variablesto create values for the unobservable factors. In the example of a survey, an individual's answersare used to generate a score for his or her opinions. In our study, each animal's metaboliteconcentrations are converted into a score for these physiological responses. Assuming that thefactors cause the variable changes, by convention it is the loading matrix that is used forinterpretation (Table 1).

Various types of FA (including principal components analysis) have been applied in studiesof psychological and psychiatric assessment (26-30), in measures of animal behavior (31,32),in face recognition and data-processing models (33,34), in analyses of microarrays and druglibraries (35,36), as well as in clinical investigations and meta-analyses to define diseaseprofiles using all the available variables (37-40). Exploratory FA can create compositemeasures for multidimensional phenomena, but the resultant structure pattern and itsinterpretation require confirmation from further studies. The validity of the factors may dependon the consistency and robustness of the procedure used, especially in deciding the number ofcomponents to retain (25). To keep the procedure simple, in this study we have followed astandard method of performing FA: identifying the principal components, retaining those thatmet the Kaiser criterion, and applying a varimax rotation. Beyond this traditional procedure,there are other methods of FA which are discussed in detail in the literature. These includevarious criteria for selecting the number of components to retain, such as the scree test, brokenstick rule, and parallel analysis, as well as several different rotation methods (25).

FA was performed on the dataset as a whole, producing factors that describe the variance acrossall 29 animals, without consideration of grouping. Only afterwards were the factors evaluatedto see if they correspond to differences between cohorts. As this type of analysis is based onthe correlation matrix between the original variables, at minimum it requires more cases thanvariables. However, most practitioners prefer 3−5 times as many cases as variables forreliability. As we performed FA on nine variables (metabolite measurements) using 29 cases(animals), this can be considered adequate in terms of the general standard.

Implications of Factor 1Factor 1 represents the contrast of Cho/Σ with GABA/Σ and NAAG/Σ. It distinguishes animalswith high Cho/Σ, low GABA/Σ, and low NAAG/Σ from animals with low Cho/Σ, high GABA/Σ, and high NAAG/Σ. Animals with moderate values of Factor 1 may have high values of allthree, low values of all three, or moderate levels of all three. Factor 1 identified the correlationsbetween these metabolites, but the ANOVA showed that the variance summarized in this factordid not differ across cohorts (Fig. 5). Therefore, it is not diagnostic of SIV/AIDS or encephalitisseverity. This factor may reflect a different source of variation, but one that we cannot speculateon at this point because of the lack of data to formulate a hypothesis. Although in vivo MRSstudies on subjects with HIV-associated dementia as well as SIV-infected macaques have

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shown a major role for choline, the same changes have rarely been observed ex vivo. Furtherinvestigation is needed to clarify whether the metabolite extraction process changes the MRSsignal of choline-containing compounds (41-43).

Implications of Factor 2The MRS patterns found in Factor 2 were associated with increasing severity of SIVE. Anexamination of the loadings for Factor 2 (Table 1) shows a significant positive contributionfrom NAA/Σ and Glu/Σ, indicating that lower NAA/Σ and Glu/Σ will contribute to a lowerscore for Factor 2, which is associated with worse disease severity. We recently reportedsignificantly lower NAA/Cr and Glu/Cr in animals with more severe encephalitis (13). Thiscorrelation is confirmed and summarized here in their combined loading in Factor 2. Both NAAand NAA/Cr are often cited as markers of neuronal integrity, and decreases in NAA and NAA/Cr have been found to correlate with severity of neuropsychological impairment in patientswith HIV (4).

Cho, MI, and Cr are known to be present at higher concentrations (∼3 times) in glial cells thanin neurons (44,45). Increases in these metabolite concentrations have been reported in subjectswith neuroAIDS (8). Although we did not discover a major contribution from Cho in Factor2, a significant negative contribution from MI/Σ was observed, as well as a moderate negativecontribution from Cr/Σ. Thus, higher MI/Σ and Cr/Σ will contribute to a lower score for Factor2, which is associated with worse disease severity. Cr, a resonance arising from creatine andthe high-energy phosphate phosphocreatine, is often assumed to be constant, and MRS studiesoften report metabolites normalized with respect to Cr. Increased MI is typically consideredto be indicative of increased glial activity, and has been reported in studies of patients withHIV (4). Increases in MI/Cr that correlate with disease severity were expected for this dataset,but no statistically significant differences in MI/Cr across classifications of disease severitywere found (13). The Cr/Σ loading in our FA offers a possible explanation of why this was notseen: the higher concentrations of MI in animals with worse encephalitis may have been maskedby the accompanying higher concentrations of Cr.

Individually, NAA/Σ, Glu/Σ, Cr/Σ, and MI/Σ are changing, but only slightly, except for NAA/Σ (Fig. 4). In assessing changes in brain metabolism of animals infected with SIV and animalswith encephalitis, the analysis is more powerful when all four metabolites are examinedtogether. Post hoc ANOVAs on individual metabolites show that a traditional univariateanalysis is not as sensitive, and would have reduced statistical power if corrections for multiplecomparisons were performed.

Most measures of spectroscopy rely on some variation in ratios (with respect to H2O, Cr, orthe sum of all metabolites) for normalization. By their very nature, these variables used fornormalization are not exempt from changing with disease. It is possible that the increases inCr/Σ and MI/Σ can be explained by a decrease in the overall sum of metabolites. However, inthe light of increases in absolute concentration of Cr and MI (ratios based on H2Oconcentration) found in the frontal cortex of human subjects with HIV infection (7,8), we haveinterpreted these results on the basis of this precedent. The possibility of changes in Cr is oftenraised as a note of caution against the use of metabolite ratios in favor of absolute concentrationsin MRS studies. Indeed, even variations across different anatomical regions must beconsidered. Although raised Cr concentrations have been observed in the frontal white andgray matter of patients with HIV, decreases were found in the basal ganglia (8). However, theuse of ratios with respect to Cr need not be discarded altogether. We have previously reportedthat NAA/Cr alone is sensitive in distinguishing between severities of encephalitis (13). OurFA suggests that the combination of lower NAA/Σ and higher Cr/Σ in the frontal cortex inanimals with more severe encephalitis actually allows NAA/Cr to be a more sensitive marker

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of encephalitis. Therefore, although the quantification of absolute concentrations is important,the use of ratios such as NAA/Cr may still be beneficial in the examination of human subjects.

Implications of Factor 3Many in vivo MRS studies at 1.5 T have tried to quantify the peaks lying between 2.0 and 2.8ppm (sometimes referred to as ‘Glx’) (8,19,22,46-50). Although Glu and Gln are the largestcontributors to this region, at lower field strengths Glx will also include resonances fromGABA, NAAG, aspartate, NAA, and succinate. Factor 3 reflects changes occurring primarilyin Glu/Σ, Gln/Σ, GABA/Σ, Gly/Σ, and Cr/Σ. Concentrations of these metabolites appear todifferentiate between animals that have SIV/AIDS and those that do not. Compared withcontrol animals, animals with AIDS exhibited a pattern of higher Cr/Σ and Gln/Σ, and lowerGlu/Σ, GABA/Σ, and Gly/Σ. The changes in Factor 3 show that in vivo measurements of theGlx region must be carefully considered. This region is very difficult to quantify in vivo at lowfield strengths, because of overlapping resonances that also extend into the base of the mainNAA peak. The data suggest that potential variations in Glx could interfere with the accuratequantification of the NAA resonance at lower fields.

Taken together, these results suggest two distinct patterns of injury in SIV/AIDS. Themetabolic markers in Factor 3 indicate that a low level of neuronal injury is occurring in allanimals with SIV/AIDS whether or not multinucleated giant cell formation is observed.Perhaps the changes summarized in Factor 3 represent the damage caused by the presence oflong-lived cells that have already established a viral reservoir in the brain at an earlier stage ora low-level steady-state influx of activated/infected monocytes (51-54). This steady insultwould contrast with the type represented by decreases in NAA, which has been reported todemonstrate not only permanent neuronal damage but also reversible injury (9,10,12). Patternsin Factor 2 – which correlate with encephalitis – may signify a more recent shift in viraldynamics. Possible explanations include the recrudescence or re-activation of infected long-lived cells (54), independent viral replication and evolution of existing genetic variants in thecentral nervous system (55), and/or a higher rate of influx of activated/infected cells from theperiphery, especially among specific subsets of blood monocytes/macrophages (51). Such ashift in viral kinetics, immune-cell trafficking, and turnover would produce more viral proteinsand pro-inflammatory factors as well as promote the formation of multinucleated giant cells.

In conclusion, the use of FA allowed the identification of two metabolite patterns, oneassociated with SIV/AIDS and the other with severity of virus-induced encephalitis. Thesepatterns would not have been observed using traditional analytical methods. This techniquealso allowed us to determine changes in metabolites that had not been reported previously (MI/Σ and Cr/Σ) in this SIV macaque model of neuroAIDS. Further work is needed to clarify theimmunological/virological basis of these metabolic patterns.

AcknowledgementsWe thank Elizabeth Curran for pathology assistance, and acknowledge the contribution of tissues from Drs Desrosiers,Kaur, Lackner, Mansfield, Shannon, and Tzipori.

Contract/grant sponsor: NIH; contract/grant number: RR13213 (RGG), NS050041 (RGG), NS34626 (RGG),RR000150 (SVW), NS051129 (MRL), EB002026 (Massachusetts Institute of Technology), RR00168−39 (NewEngland Primate Research Center), and P41 RR14075 (Massachusetts General Hospital).

Contract/grant sponsor: Mental Illness and Neuroscience Discovery (MIND) Institute.

Abbreviations usedAIDS, acquired immunodeficiency syndrome

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ANOVA, analysis of varianceCho, choline-containing compoundsCr, creatineFA, factor analysisGABA, γ-aminobutyric acidGln, glutamineGlu, glutamateGly, glycineHIV, human immunodeficiency virusMI, myo-inositolNAA, N-acetylaspartateNAAG, N-acetylaspartylglutamateSIV, simian immunodeficiency virusSIVE, SIV-induced encephalitisΣ, sum of all metabolites of interest

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Figure 1.600 MHz 1H MR spectrum of frontal cortex metabolites from a healthy rhesus macaque.Signals arising from the proton resonances used in this analysis are labeled: choline-containingcompounds (Cho), creatine (Cr), myo-inositol (MI), glycine (Gly), γ-aminobutyric acid(GABA), glutamine (Gln), glutamate (Glu), N-acetylaspartylglutamate (NAAG), N-acetylaspartate (NAA), and acetate (Ace). Resonances marked with † contribute to Factor 2,and those marked with ‡ contribute to Factor 3. The main Cr y and NAA resonances weretruncated to allow a clearer view of the resonances with smaller peaks.

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Figure 2.Factor 3 as a predictor of SIV/AIDS. Factor 3 represents a linear combination that summarizesmetabolic changes in Cr/Σ, GABA/Σ, Gln/Σ, Glu/Σ, and Gly/Σ in the frontal cortex in SIV-infected animals. Animals with SIV/AIDS had significantly lower scores for Factor 3 thanhealthy controls (left), but this factor was not able to distinguish between classifications ofSIVE severity (right). Error bars represent standard error of the mean.

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Figure 3.Factor 2 as a predictor of encephalitis severity. Factor 2 represents a linear combination thatsummarized major metabolic changes in both neuronal (NAA/Σ, Glu/Σ) and glial (Cr/Σ, MI/Σ) cells during SIV infection. Animals with SIVE had significantly lower scores for Factor 2than those without encephalitis (left). Factor 2 was able to distinguish animals with moderateto severe encephalitis from animals with mild or no encephalitis as well as uninfected controls(right). Error bars represent standard error of the mean.

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Figure 4.Ratio of metabolites/total spectrum. Mean values and standard error of the mean are presentedto demonstrate individual metabolites across classifications of encephalitis severity. Cr/Σappears to be increased in all animals infected with SIV and moribund with AIDS. Glu/Σ showsthe opposite trend. For the severest disease classifications, MI/Σ increases while NAA/Σdecreases. Cho/Σ does not differ significantly across the cohorts. FA captures these effects incombination, and is more powerful than traditional univariate analyses, as revealed by posthoc ANOVA (Cr/Σ: P = 0.07; NAA/Σ: P = 0.0006; Glu/Σ: P = 0.05; MI/Σ: P = 0.005; Cho/Σ:P = 0.29) uncorrected for multiple comparisons.

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Figure 5.Factor 1 is not diagnostic of SIV/AIDS or encephalitis severity. Factor 1 represents a linearcombination that summarizes metabolic changes in Cho/Σ, GABA/Σ, and NAAG/Σ. Scoresfor Factor 1 did not significantly differ among cohorts. Error bars represent standard error ofthe mean.

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Table 1Factor loadings

Factor 1 Factor 2 Factor 3

Variance explained Eigenvalue 1.615 2.304 2.093 Percentage 17.949 25.598 23.250Loadingsa Cr −0.285 −0.479 −0.637 NAA −0.130 0.884 0.013 Glu 0.246 0.689 0.564 Gln −0.195 0.209 −0.697 GABA −0.412 0.229 0.679 Gly −0.240 −0.030 0.633 NAAG −0.879 −0.062 0.078 Cho 0.596 −0.248 0.021 MI 0.252 −0.810 0.118

aStrong (0.6+) or moderate (0.4−0.6) loadings are shown in bold to indicate the metabolites involved in each factor. Concentrations of each metabolite

were calculated by dividing its area by the total area of all metabolites of interest (Met/Σ).


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Table 2Histopathological classifications and factor scores

Animal Central nervous system pathologya Factor 1 Factor 2 Factor 3

C1 Healthy seronegative control 0.818 1.533 0.930C2 Healthy seronegative control −0.108 1.588 1.380C3 Healthy seronegative control −0.420 1.386 0.045C4 Healthy seronegative control 0.521 0.847 0.805C5 Healthy seronegative control −0.116 0.253 1.706C6 Healthy seronegative control 0.723 −0.235 0.274

M1 Severe encephalitis −0.574 −0.265 −0.079M2 Severe encephalitis 0.434 −1.106 0.532M3 Severe encephalitis 1.295 −1.131 0.728M4 Severe encephalitis 0.572 −1.907 0.389M5 Severe encephalitis −0.364 0.553 −0.765M6 Severe encephalitis −0.005 −1.299 −1.882M7 Atypical severe encephalitis 1.228 −1.370 0.044

M8 Moderate encephalitis 0.301 −1.792 0.487M9 Moderate encephalitis −0.422 0.484 0.989

M10 Moderate encephalitis −0.732 −0.511 −0.319M11 Moderate encephalitis 1.197 −0.842 0.351

M12 Mild encephalitis −4.029 −0.848 0.307M13 Mild encephalitis −1.053 −0.166 0.784M14 Mild encephalitis −0.412 0.153 −2.453M15 Mild encephalitis 0.415 1.661 −1.326M16 Mild encephalitis 0.182 0.566 0.273M17 Mild encephalitis 0.795 0.072 −0.668

M18 No encephalitis 0.362 0.381 −0.513M19 No encephalitis 0.142 0.633 −0.259M20 No encephalitis −0.286 0.322 0.449M21 No encephalitis 0.761 1.046 −1.709M22 No encephalitis −0.499 0.373 0.862M23 No encephalitis −0.724 −0.377 −1.363

aNote: M1-M23 were all inoculated with either SIVmac251 or SIVmac239, and all were sacrificed because they were moribund with AIDS.


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