Gene Expression in HIV-Associated Dementia
Paul Shapshak, Alireza Minagar, Elda M. Duran, Fabiana Ziegler, Wade Davis, Raman Seth, and Toni Kazic
1. INTRODUCTION
In past decades, the prime methods used for the study of genes and their respective products were Northern blots, Western blots, in situ hybridization, and immunocytochemistry. The applica- bility of these methods was restricted to the study of single genes or a few simultaneously (1,2).
With the development of gene expression and bioinformatics technology, we can finally eas- ily identify crucial interactions between viral proteins and the cellular proteins responsible for the establishment and perpetration of the human immunodeficiency virus (HIV) life cycle, par- ticularly in the brain. DNA and protein microarrays, gene chips, or biochips hold great promise for discovery of novel molecular and cellular mechanisms of neurological disease because they allow the simultaneous rapid and parallel assessment of thousands of genes (2,3). Gene expres- sion uses microscopic amounts of DNA, oligonucleotide, or antibody probes arranged in very small grid-like organization. The instruments that handle samples (including automated robotics) read the reporter molecule intensities (scanning). In addition, methods to analyze the data (bioin- formatics and statistics) are in progress. These technologies are revolutionizing gene expression analysis by providing hybridization-based RNA and protein monitoring, polymorphism detec- tion, and phenotyping on a genomic scale. Microarray technology is being extensively devel- oped and applied to study gene expression in several neurological and psychiatric disorders (2,4). We review gene expression studies of HIV-1 associated dementia (HAD), as well as the need for improved bioinformatics methods.
2. HIV-ASSOCIATED DEMENTIA
The determination of the basic mechanisms of the pathogenesis of HAD remains unsolved.
HIV-1 infection is the cause of HAD, which presents with progressive encephalopathy. HAD also presents in the absence of detectable primary effects of other pathogenic agents or conditions, including opportunistic infections and lymphoma, but with HIV-related impairment of normal cerebral function. HAD affects 20 to 50% of patients with acquired immunodeficiency syndrome (AIDS) who are not taking therapy and 5 to 10% of patients with AIDS are taking highly active antiretroviral therapy (5–8).
Torres-Munoz et al. (9) demonstrated cytopathic effects of HIV-1 on neurons using laser capture microdissected neurons in the hippocampus consistent with latent infection by HIV-1. Thus, neuronal
305
From: Current Clinical Neurology: Inflammatory Disorders of the Nervous System:
Pathogenesis, Immunology, and Clinical Management
Edited by: A. Minagar and J. S. Alexander © Humana Press Inc., Totowa, NJ
infection may contribute to pathology because of the vulnerability of neurons to HIV-1 infection.
Neuronal HIV infection could contribute to neuronal injury and death and may be mediated through cytokine and chemokine production (10).
The basic mechanisms of pathogenesis involving brain cells remain largely unknown. Perhaps events external to the central nervous system (CNS) that activate moncytes initiate a priming pro- cess that leads to HAD (11). Certainly, there is a compartmentalization of HIV evolution in the brain (7,12,13), and HIV recombination occurs frequently in the brain (14). Brain tissue is an HIV infection reservoir and compartment. Furthermore, data suggests that HIV-1-associated neurological disease is related to the level of viral infection in activated macrophages in humans and in a mouse model (15–17). As examples of studies in the human brain, we show recently stained brain tissue from subjects who were HIV-1 infected (Figs. 1–3).
Astrocytes may have a major role in the pathogenesis of HAD and recently were studied in the context of AIDS by Galey et al (18). Microarray analysis and ribonuclease protection assays were used following HIV infection or treatment with gp120. 1,153 oligonucleotides were used on an immune-based array and found 108 genes (53 upregulated, 55 downregulated) changed by gp120 treatment and 82 genes (32 upregulated, 50 downregulated) changed by HIV infection. Of the 1153 oligonucleotides on the neurobased array, 58 genes (25 upregulated, 33 downregulated) were altered by gp120 treatment and 47 genes (17 upregulated, 30 downregulated) were altered by HIV infection.
Gp120 treatment downregulated tumor necrosis factor (TNF)-receptor-associated factor 3, and HIV-1 infection downregulated expression of TNF-ligand and interleukin (IL)-1b but induced expression of IL-15. The decreased importance for the TNF system in HIV infection reported by Galey et al. (18) is consistent with results in brains of patients with AIDS (19,20). Progeny HIV-1 virions affect neuronal signal transduction and apoptosis through the CXCR4 receptor, independent of CD4 binding (21). Apoptosis via BCL2-associated X protein (BAX) expression is a well-described pathway. Intracellular HIV protein expression might promote apoptosis via transcription of BAX, and extracellular treatment of astrocytes with gp120 downregulates BAX in astrocytes (18).
Table 1 shows a summary selection of genes from Gayley et al. (18).
Fig. 1. Formalin-fixed paraffin-embedded (FFPE) section from white matter associated with Globus Pallidus from male Caucasian patient, age 50 yr, HIV-1 seropositive, homosexual, without HIV-associated dementia (HAD), and without HIV encephalitis (HIVE). Five micron thickness, original magnification× 220, Hematoxylin and Eosin. Arrows: oligodendrocytes.
HIV-1-host interaction studies commenced recently using microarrays. Geiss et al. (22) performed early HIV-1 infection-related quantitative analyses of host gene expression during HIV-1 infection of the CEM-CCRF CD4
+T-cell line. They identified 20 cellular mRNAs using 1506 cDNAs. Pathways for T-cell receptor-mediated signaling, subcellular trafficking, transcriptional regulation, host-cell defense, facilitation of the viral life cycle, and a variety of cellular metabolic pathways were pinpointed, based on differential gene expression. Table 2 shows a summary selection of genes from the Geiss et al. study (22).
At the Fox laboratory, HIV-related hypotheses were developed and tested by Roberts (23) using an SIVmac182 and Rhesus macaque animal model to characterize brain gene expression 70 to 110 d postinfection. CNS abnormalities and neuropathology occurred in all the infected monkeys.
Sophisticated methods of immunohistochemistry and in situ hybridization in monkey brain sections evaluated genes that changed in expression using human Affymetrix arrays and genes (23). A selec- tion of genes in the Roberts et al. (23) study is summarized in Table 3. One gene is exemplified in each category (out of 98 genes).
In a related study in the brain of macaques with simian HIV (SHIV)-encephalitis, Sui et al.
(24) performed microarray analysis of chemokine and cytokine genes in reference to HIV encephalitis, including macrophage/microglia activation, astrocytosis, neuronal dysfunction, and death. They produced gene-expression profiles in SHIV 89.6P-infected macaques. Of 277 genes
Fig. 2. Cryosection from Globus Pallidus from HIV-1 seropositive patient with HIV-associated dementia and HIV encephalitis, age 47 yr. Perivascular inflammation and neurons are shown. Five micron thickness, original magnification× 220, Hematoxylin and Eosin. Small arrow: neuron; medium arrow: blood vessel;large arrow: macrophage/monocyte.
screened in the encephalitic brains, several proinflammatory genes were upregulated: monocyte chemoattractant protein-1, interferon-inducible peptide IP-10, and IL-4, which are genes that pro- mote virus replication, macrophage infiltration, and activation. Nonetheless, there was downregu- lation of neurotrophic function-related genes, including brain-derived neurotrophic factor, although apoptosis genes were up- or downregulated. Gene expression in the SHIV-macaque model results in dystrophic changes in neurons and enhanced virus replication in brain macrophages, according to Sui et al. A selection of results from Sui et al. is shown in Table 4 (24).
Another animal study also produced information in regard to neuroAIDS using peripheral blood mononuclear cells (PBMNCs) from Rhesus macaques infected with SIVmac251.25. Gene expression in PBMNCs was analyzed in infected animals exhibiting rapid, typical, or slow rates of disease progression. They used fold change, Z score, and Self Organizing Map (SOM) data analysis. Among the three progession groups, gene expression showed different patterns involving fold change, variation between groups, and temporal coordination of gene expression.
Immune response and cytoskeleton genes were elevated in the typically vs rapidly progressing group. Rapid course of disease was associated with some increase in but a lack of coordination of gene expression. Rapid and typical groups showed minimal differences in categories and numbers of genes expressed; however, differences between the rapid and slow groups were more distinct. Typical group gene expression demonstrated the largest numbers of gene expression changes in expression and some coordination of their expression, although typical and slow groups showed less distinct differences than the rapid and slow groups did. The slow group showed the lowest degree of gene-expression change and the maximum coordinated gene expression. Possibly, the authors conclude, the changes are indicative of gene expression specific for each type of disease progression and also for gene expression specific to a drive for survival of the postinfection host.
Fig. 3. Cryosection of Globus Pallidus from female black patient, age 64 yr, intravenous drug user, HIV-1 seropositive, with HIV-associated dementia, and without HIV encephalitis. Large arrow: neuron; small arrow: macrophage/microglia. Five μ thickness, original magnification × 300, Nissl stain. The neuron cytoplasm is less visible because the section was stained for only 60 s. The section was then used for laser capture microdissection.
A preliminary study comparing brains with AIDS and normal brains was done to test hypothe- ses related to known genes involved in neuroAIDS and for gene discovery for related genes, as well as for unanticipated genes. These are important issues in the neuroAIDS field because hypothesis-driven research is considered its cornerstone. Changes are occurring in this field because of the tremendous advantage of the new microarray technology and its attendant develop- ment of bioinformatics. Initial assessments of gene expression in brain tissue from patients with AIDS vs control (HIV-negative) patients indicated a wide range of unanticipated changes in gene expression, including cell-signaling, Notch 3, and homeobox gene changes, as well as anticipated inflammation-related genes (4,26,27). Genes identified by taking ratios of gene expression (whose significance was confirmed by Mann-Whitney analysis) between AIDS and controls were different from those identified by K-means cluster analysis (27). A few examples are shown in Tables 5 and 6. (It should be noted that gene expression identification was performed through the NetAffx Analysis Center of Affymetrix Corp [28].) This suggested to us that additional studies are required to compare significance among various bioinformatics approaches. We dis- cuss this in the next section.
Table 1
Astrocyte Culture Gene Expression Changesa
Accession number Gene ID p value Z ratio
Upregulated by gp120 treatment
AA284528 Protease, Serine, 2 (Trypsin 2) 2.22E-16 2.14
NM_001028 Ribosomal protein S25 5.29E-05 1.91
NM_007315 STAT 1 4.83 E-04 1.56
NM_000484 APP 9.7E-4 1.56
Upregulated by HIV-1 infection
NM_004324 BAX 1.04E-11 2.30
NM_002545 Opioid binding protein 0 3.67
NM_001225 Caspase 4 1.31E-13 2.83
NM000416 IFNGR1 5.54E-4 2.03
Downregulated by gp120 treatment
NM_004324 BAX 0 −1.73
NM_002545 Opioid-binding protein 0 −3.12
AA069414 GFAP 0 −3.09
R98851 Membrane metalloendopeptidase 0 −2.93
(enkephalinase)
NM_003300 TRAF3 0 −3.75
Downregulated by HIV-1 infection
AA284528 Protease, Serine, 2 (Trypsin 2) 0 −2.79
NM_001028 Ribosomal protein S25 3.2E-4 −3.07
AA069414 GFAP 0 −6.73
R98851 Membrane metalloendopeptidase 2.05E-5 −2.00
(enkephalinase)
NM_000576 IL-1b 4.69E-5 −1.94
NM_003807 TNF (ligand) superfamily, 1.24E-4 −1.92
member 14
STAT 1, signal transducer and activator of transcription 1; APP, amyloid β (A4) precursor protein; BAX, BCL2- associated X protein; IFNGR1, interferon-γ receptor 1; GFAP, glial fibrillary acidic protein; TRAF3, TNF receptor- associated factor 3.
aMethods for p-value and Z-ratio calculations are described in Galey et al. (18).
Table 2
Peripheral Blood Mononuclear Cell Culture Gene Expression Changes
Fold change
Accession number Gene ID Day 2 PI Day 3 PI
Upregulated
AA427491 T-cell receptor α-chain, c region 1.19 ± 0.14 3.13 ± 0.77
AA443584 CD8-α precursor 0.99 ± 0.14 1.63 ± 0.24
AA455278 Human neurogranin 1.02 ± 0.07 1.68 ± 0.24
AA458503 RIP140 0.84 ± 0.10 1.74 ± 0.17
AA457123 Transfer valyl-tRNA synthetase 0.89 ± 0.13 1.68 ± 0.14
AA457118 HTOM34 translocase 1.03 ± 0.13 1.82 ± 0.12
AA290698 Image ID 700414 1.21 ± 0.14 2.23 ± 0.27
H47026 Image ID 178468 0.93 ± 0.16 1.52 ± 0.11
AA455695 Image ID 813990 1.10 ± 0.09 1.66 ± 0.25
Downregulated
AA455931 Adenylosuccinate lyase 1.00 ± 0.21 0.65 ± 0.02
M26708 Prothymosin-α 0.81± 0.14 0.48 ± 0.04
L00160 Phosphoglycerate kinase 1.17 ± 0.21 0.58 ± 0.13
AA401111 Glucose phosphate isomerase 1.04 ± 0.07 0.51 ± 0.04
AA621300 P18 protein mRNA/stathmin 0.95 ± 0.12 0.60 ± 0.10
AA463631 Signal recognition particle 72 0.66 ± 0.16 NDa
AA399473 PP5 ND* 0.67 ± 0.09
AA504327 Type IVA protein tyrosine phosphatase 0.83 ± 0.13 0.57 ± 0.06
U34379 Txk kinase 0.65 ± 0.15 ND
AF038953 E25 mRNA clone 1 1.21 ± 0.13 0.57 ± 0.04
E25 mRNA clone 2 1.15 ± 0.08 0.61 ± 0.06
aND, not determined because of insufficient signal above background. PI, postinfection; RIP140, receptor- interacting protein 140; PP5, placental protein 5. (From ref. 22.)
Table 3
Rhesus Macaque Brain Gene Expression
Upregulated category Accession
(no. of genes in category) number Gene ID Fold change
Monocyte migration (10) AF052124 Osteopontin 3.4
Inflammation and disease (17) X68733 α1-antichymotrypsin 2.6
Antigen presentation (18) M32578 HLA-DRβ1 4.4
Lysozomal (7) U51240 LAPTM-5 2.9
Interferon inducible (13) U22970 G1P3 8.4
Antiviral (3) X79448 Adenosine deaminase 2.6
Transcriptional regulation (11) X52560 NF-IL6 2.5
Cell cycle (7) M92287 Cyclin D3 2.1
Growth, differentiation, signaling (8) X66945 FGFR1 2.5
Cytoskeleton (6) X53587 Integrin β 4 2.1
Immune system AI142565 Ribonuclease K6 2.6
There were seven additional genes detected not in the above categories. Similar changes in several of these genes were also detected in other brain regions analyzed. (From ref. 23.)
Table 4
Some Changes in Gene Expression by Ratio of Encephalitic vs Nonencephalitic SHIV-Positive Brain Tissue
Category Gene name Ratio of expression change
Interleukins and interferons
IL-4 3.05
IL-1 receptor 1A 0.64
Chemokines
Leukocyte IP-10 8.4
MIP-1A receptor 1.62
RANTES receptor 1.62
Neurotropic growth factors
BDNF 0.22
Neuromodulin 0.53
GDNPF 0.66
Protein kinase
ERBB-3 protein protein-tyrosine kinase 0.69 Apoptosis-related protein
CD27BP (Siva) 2.79
TNF-R 1.86
CD30L 0.64
CD40L 0.29
IL, interleukin; IP, interferon-inducible peptide; TNF, tumor necrosis factor.
Table 5
Genes Indicated by K-Means Cluster Analysisa 3AL031670: ferritin, light polypeptide-like 1 M13577: myelin basic protein (MBP) D29675: inducible nitric oxide synthase gene M54927: myelin proteolipid protein U12022: calmodulin (CALM1)
U48437: amyloid precursor-like protein 1 L20941: ferritin heavy chain
L13266: N-methyl-D-aspartate receptor (NR1-1) M58378: synapsin I (SYN 1) gene
M33210: colony stimulating factor 1 receptor M21121: T cell-specific protein (RANTES) M29273: myelin-associated glycoprotein (MAG) X86809: major astrocytic phosphoprotein PEA-15 L13268: N-methyl-D-aspartate receptor (NR1-3) U97669: Notch 3
U22970: interferon-inducible peptide (6-16) gene D31815: senescence marker protein-30
AF039555: visinin-like protein 1 (VSNL1) AB015202: gene for hippocalcin
D88799: cadherin, partial cds AL022326: Synaptogyrin 1A U31767: neuronatin α & β genes
These genes were in different cluster groups when comparisons were made between AIDS and HIV-negative controls brains. (From ref. 27.)
3. BIOINFORMATICS FOR HIV-ASSOCIATED DEMENTIA
The previously discussed studies (18,22–25,27) demonstrate the power of gene expression analysis in the study HAD but also illustrate the need for improved bioinformatics tools. This need is not unique to HAD—this need pervades genomic research. The demand for mature bioin- formatics tools has never been greater, and innovation must continue in order to keep pace with evolving technology.
The credibility of results obtained using state-of-the-art gene expression is only as good as the design of the experiment and the statistical methods employed to analyze such daedal data. Kerr et al. (29) emphasize that the structure of the data, the number of possible analyses, and the quality of the results are determined by the experimental design. Furthey, they prove that a commonly used design for cDNA array experiments is inefficient and may introduce confounded effects, and they propose a superior design.
Another factor critical to the analysis is the allocation of samples to microarrays with respect to the type and level of replication. Given the high cost of gene chips and the scarcity of tissue samples, the need for efficient resource allocation is essential. Zien et al. (30) provide some practical advice in this regard via interactive Monte Carlo simulations.
The challenging aspects of analyzing gene expression data are caused its high dimensionality.
The latest human chip sets can target more than 33,000 genes but rarely more than 60 observa- tions (chips) are collected in an experiment and, in fact, the typical number is less than 20 chips.
Traditional classification algorithms cannot cope with such a large number of variables (genes) and so few observations; thus, modifications of existing procedures or the development of new methods are needed to address these issues. One solution is to use dimension-reduction techniques.
Existing-dimension reduction techniques, such as principal components analysis, partial least squares, and singular value decomposition, have been used in analyzing microarray data (31–33).
Table 6
Genes Ranked by Ratios of HIV+/HIV-a
Gene Description Ratio Ratio rank
AF062739: GSK-3 binding protein FRAT2 0.140941 1
M61156: activator protein 2B (AP-2B) 0.142538 2
D00096: prealbumin, (26,469) 0.143123 3
D78514 ubiquitin-conjugating enzyme 0.155713 4
J04131:γ-glutamyl transpeptidase (GGT) protein 0.158651 5
AF030107: regulator of G protein signaling (RGS13) 0.165479 6
L23805 CATENINα1(E)-catenin 0.168352 7
AF082558: truncated TRF1-interacting ankyrin-related 0.168513 8
ADP-ribose polymerase TT7
U40572:β2-syntrophin (SNT B2) 7.2271 12569
AL031652: dJ1119D9.1 (PAK1-like serine/threonine-protein kinase) 7.595604 12571 M63438: Ig rearranged γ chain, V-J-C region and (0,1049) 8.058278 12572
M27504 TOPIIX topoisomerase type II (Topo II) 8.585115 12574
U06632: p80-coilin 8.685422 12575
AJ223948: putative RNA helicase 9.021651 12576
D86864: acetyl LDL receptor 9.109537 12577
X97324: adipophilin 9.171688 12578
Y14737: immunoglobulin λ heavy chain 9.224281 12579
L10717 TKTCST cell-specific tyrosine kinase 9.260073 12581
aRatios of gene expression intensity were produced for this examination and confirmed by Mann-Whitney analysis.
Ig, immunoglobulin; LDL, low-density lipoprotein.
These methods have been implemented in conjunction with pattern recognition algorithms to classify disease states based on gene expression (32).
Cluster analysis is popular a bioinformatics tool for identifying gene expression profiles associated with different diseases or disease states (25,27,34). Although cluster analysis traces its roots back to the sixties and early seventies, it has received a great deal of attention in the lit- erature since Eisen et al. (34) popularized its use in bioinformatics circles. Shapshak et al. (27) used K-means clustering to study gene expression in brain tissue from patients with AIDS vs control (HIV-negative) patients, whereas Vahey et al. (25) used SOM clusters to examine pat- terns of gene expression associated with the rate of disease progression in Rhesus macaques infected with SIVmac251. It is important to note that clustering is an exploratory tool rather than a confirmatory tool, and statistical inference cannot be drawn from the application of such methods; however, cluster analysis is a powerful instrument for the exploration of gene-expression analysis.
Careful hypothesis testing is necessary to draw valid statistical inferences from properly designed microarray experiments. The primary aim of most studies is to identify which genes exhibit upregulation or downregulation owing to pathogenesis. A myriad of inferential procedures have been proposed for determining these differentially expressed genes, many of which are mod- ifications of basic statistical methods, such as the t-test or analysis of variance. Before any tests are conducted, careful preprocessing is recommended, especially when dealing with cDNA arrays (35).
Recent work by Giles and Kipling (36) suggests that Affymetrix data may not need as much preprocessing as had been previous thought. Following preprocessing, the level of significance for each individual test must be adjusted to account for large number of genes being tested.
Otherwise, the family-wise error rate becomes unacceptable. Multiple correction procedures are used to compute this adjustment, and recent methods have focused on controlling the false dis- covery rate. Tusher et al. (37) propose the significance analysis of microarrays as one method for achieving this. This is an active area of research and will continue to receive attention in the bioinformatics literature.
Bioinformatics for neuroAIDS poses three types of challenges. The first type is related specifically to microarray experiments, rather than to neurological or viral diseases. The second type revolves around the explosive growth in images, atlases, and databases of neurobiological information in the last 15 yr. The third type is truly global: how to let these different resources talk sensibly with each other, so that people can use this cornucopia and input it into algorithms to understand and treat diseases. We briefly discuss each of these in turn.
First, the microarray-specific problems involve the acquisition of the experimental data and
their processing and analysis (a good introduction is found in ref. 38). Systems for acquiring and
storing microarray data take two forms: laboratory information management systems (LIMS)
and publicly accessible repositories of information. In addition to recording the experimental
data, fully functional LIMS support experimental design, protocols, and tracking; recording
information on sample sources, preparation, and usage; and inventory control, quality control,
and appropriate data security. Although a number of different open source systems have been
developed, each support some but not all of these desiderata (e.g., see refs. 39–42). For example,
BioArray Software Environment (BASE) makes no attempt to handle process-related informa-
tion or inventory; Stanford Microarray Database (SMD) and its descendant Longhorn Array
Database (LAD) concentrate mainly on the intensity data and descriptions of protocols, and sam-
ples are relatively weak and buried in textual comments. SMD is used to support a publicly
accessible repository of microarray data, and several other groups have developed repositories
and portals that similarly support a variety of queries via websites (43–49). We have already dis-
cussed some of the issues involved in processing the acquired data; indeed, an analytical pipeline
that permits the use of a variety of algorithms and the incorporation of new ones has been
developed for just this reason (50).
The second type of challenge is to build useful models of brain function that incorporate what we know and stimulate discovery. In the last 15 yr, the neuroscience community has seen an explosion of information about the brain and immediately understood that databases of this infor- mation would be absolutely vital. Images, functional magnetic resonance imaging data, anatomi- cal connections, cortical surfaces, and computational models are just a few of many of types of data electronically available (51–55). Understanding how the brain works and the etiology of neurological diseases, such as HAD, necessarily draws on all this information. It is difficult to fit the pieces together into a partial model that explains the mechanism of the phenotype well enough to be worth pursuing but leaves ample room to expose gaps in both the data and the model. For example, suppose gene X is identified in a microarray experiment as being upregulated in HAD from post mortem brain tissue. Does this gene contribute to the dementia, or is the elevation of its expression incidental to the disease? In evaluating these possibilities, an investigator might con- sider information on the histological and cytological localization of the gene’s product in normal brains and similarities in brain images from patients with late-stage HAD or Alzheimer’s disease.
Finding these data in a targeted search is a significant challenge; uncovering data that may be relevant but that are unknown to the investigator is even more difficult, and being able to recognize if infor- mation on whether gene product copurifies with neurofibrillary tangles is missing is even more difficult. When the amount of data is small, the models are compact enough to evaluate on the back of an envelope, although with our present embarrassment of riches, the models and the techniques for discovering the important positive and negative relationships, as well as nonrelationships, among the data will require computation.
The third challenge is generic to many problems, systems, and databases: how to can databases talk sensibly with each other to allow algorithmically pursuit of questions from many different sources (semantic interoperability). This is distinct from the second challenge of building models because, in this case, the data for stocking the model is available. Humans can converse indefinitely until each person understands what the other means by a particular word or phrase, but computers obviously cannot yet rely on natural language processing to interpret terms. Some investigators have avoided the problems of interoperability by manually translating schema and semantics of different databases into an integrated warehouse or clus- ter of databases (39,43,48,54). The difficulty with this approach is that it does not scale, both as a function of the number of databases and their complexity. Thus, methods of achieving semantic interoperability have been the focus of intensive work for several years. There are two main approaches to achieving interoperability: the suggestion of standard terms and notions and the development of formal methods to declare and interconvert the semantics of complex ideas. Regarding the first suggestion, there have been substantial efforts in the microarray, bioinformatics, and the neuroinformatics communities. In the microarray commu- nity, a set of minimal descriptors and a basic schema for microarray databases have been sug- gested to facilitate data exchange (minimum information about a microarray experiment [MIAME] and microarray gene expression [MAGE], respectively; see references 56–60).
SMD and LAD predate the MIAME and MAGE proposals, and BASE does not strictly follow MAGE (39–41). The Gene Ontology seeks to develop a classification of terms, along with definitions, that can be used to denote cellular structures, physiological processes, and molec- ular functions and will be broadly useful in databases of molecular and some organismal infor- mation (61–62). In its earliest days, the neuroinformatics community has understood interoperability to be a central challenge (63–65). Proposed solutions range from standardized schemata (66–65) and anatomical nomenclature (67–69) to automatic generation of databases using the same schema (70).
As yet, no standards have been uniformly adopted by the relevant databases. Indeed, there are
significant practical problems in retrofitting new standards to existing databases, since usage and
the organization of concepts varies so widely among databases; retrofitting is necessarily a manual
operation that often involves restructuring the entire database. The usage of a term in a database may not correspond to its new definition or its definition may be vague. Databases that evolve with a standardized set of ideas obviously do not need retrofitting, but they do need to conform closely to the standards if interoperability is to be preserved. For these reasons, an alternative approach that uses formal methods to declare semantics remains of interest (71–73). If successful, such methods would provide an abstract semantic layer that databases could use to translate each other’s ideas into their own, eliminating the need for retrofitting or compliance with a fixed view of biology.
4. CONCLUSION
Absolute and conclusive logic would be premature at this point because previous studies were on different types of cells and tissues, used different analytic algorithms, and were not prospec- tively designed. Future reviews will address these issues as data becomes available. Despite prob- lems with previous studies, noteworthy parallels have been found in the disparate studies that are both enigmatic and suggestive of future direction. Thus, this chapter indicates direction taken by prior studies in their analysis of neuroAIDS-related gene expression.
ACKNOWLEDGMENTS
We thank Carol K. Petito, MD for neuropathology discussions. We gratefully acknowledge the NIH National NeuroAIDS Tissue Consortium for providing brain tissue. This work was supported in part by NIH grants DA 14533, DA 12580, DA 07909, DA 04787, and AG 19952 (to P.S.) and GM-56529 (to T.K. and P.S.)
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CME QUESTIONS
1. Which statement about human immunodeficiency virus (HIV)-associated dementia (HAD) is correct?
A. HAD presents with a progressive encephalopathy with impairment of normal cerebral function, and despite major advances in our understanding of the pathogenesis of HAD, the basic mechanisms of the pathological and direct involvement of essential cells, such as neurons, astrocytes, and invading macrophages still remain largely unknown
B. HAD occurs only as a late manifestation of acquired immunodeficiency disease C. HAD presents initially with apraxia and aphasia
D. HAD affects males more often than females
2. Which statement about microarray gene expression analysis is correct?
A. It allows us to examine the expression of thousands of genes at once B. It allows us to look at the expression of only hundreds of proteins C. It has never been used in the study of HAD
D. It allows us to calculate the molecular weight of gene products, particularly proteins 3. Which of these cells has a role in the pathogenesis of HAD?
A. Astrocytes
B. Microglia/macrophages C. Neurons
D. All of the above
