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The pathogenesis and pathophysiology of a disease determine how it should be diagnosed and treated. Yet, understanding the cause and mechanisms of progression often requires intensive human efforts, especially for diseases with complex etiology. The latest genomic technology coupled with advanced, large-scale data analysis in the field known as bioinformatics has promised a high-throughput approach that can quickly identify disease-affected genes and pathways by examining tissue samples collected from patients and control subjects. Furthermore, significant biological themes indicative of genomic events can be recognized on the basis of affected genes. However, given identified biological themes, it is not clear how to organize genomic events to arrive at a coherent pathophysiological explanation about the disease. To address this important issue, we have developed an innovative method named "Expression Data Up-Stream Analysis" (EDUSA) that can perform a bioinformatics analysis to identify and rank upstream processes effectively. We applied it to Parkinson's disease (PD) using a genomic data set available at a public data repository known as Gene Expression Omnibus (GEO). In this study, disease-affected genes were identified using GEO2R software, and disease-pertinent processes were identified using EASE software. Then the EDUSA program was used to determine the upstream versus downstream hierarchy of the processes. The results confirmed the current misfolded protein theory about the pathogenesis of PD, and provided new insights as well. Particularly, our program discovered that RNA (ribonucleic acid) metabolism pathology was a potential cause of PD, which in fact, is an emerging theory of neurodegenerative disorders. In addition, it was found that the dysfunction of the transport system seemed to occur in the early phase of neurodegeneration, whereas mitochondrial dysfunction appeared at a later stage. Using this methodology, we have demonstrated how to determine the stages of disease development with single-point data collection.

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Tuberculosis (TB) remains to be a global major public-health threat, causing millions of deaths each year. A major difficulty in dealing with TB is that the causative bacterium, Mycobacterium tuberculosis, can persist in host tissue for a long period of time even after treatment. Mycobacterial persistence has become a central research focus for developing next-generation TB drugs. Latest genomic technology has enabled a high-throughput approach for identifying potential TB drug targets. Each gene product can be screened for its uniqueness to the TB metabolism, host-pathogen discrimination, essentiality for survival, and potential for chemical binding, among other properties. However, the exhaustive search for useful drug targets over the entire genome would not be productive as expected in practice. On the other hand, the problem can be formulated as pattern recognition or inductive learning and tackled with rule-based or statistically based learning algorithms. Here we review the perspective that combines machine learning and genomics for drug discovery in tuberculosis.

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Tuberculosis is a leading infectious disease causing millions of deaths each year. How to eradicate mycobacterial persistence has become a central research focus for developing next-generation TB drugs. Yet, the knowledge in this area is fundamentally limited and only a few drugs, notably capreomycin and PA-824, have been shown to be active against non-replicating persistent TB bacilli. In this study, we performed a new bioinformatics analysis on microarray-based gene expression data obtained from the public domain to explore genes that were differentially induced by drugs between the group of capreomycin and PA-824 and the group of mainly the first-line TB drugs. Our study has identified 42 genes specifically induced by capreomycin and PA-824. Many of these genes are related to stress responses. In terms of the distribution of identified genes in a specific category relative to the whole genome, only the categories of PE/PPE and conserved hypotheticals have statistical significance. Six among the 42 genes identified in this study are on the list of the top 100 persistence targets selected by the TB Structural Genomics Consortium. Further biological elucidation of their roles in mycobacterial persistence is warranted.

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BACKGROUND: Tuberculosis remains a leading infectious disease with global public health threat. Its control and management have been complicated by multi-drug resistance and latent infection, which prompts scientists to find new and more effective drugs. With the completion of the genome sequence of the etiologic bacterium, Mycobacterium tuberculosis, it is now feasible to search for new drug targets by sieving through a large number of gene products and conduct genome-scale experiments based on microarray technology. However, the full potential of genome-wide microarray analysis in configuring interrelationships among all genes in M. tuberculosis has yet to be realized. To date, it is only possible to assign a function to 52% of proteins predicted in the genome. RESULTS: We conducted a functional-genomics study using the high-resolution Affymetrix oligonucleotide GeneChip. Approximately one-half of the genes were found to be always expressed, including more than 100 predicted conserved hypotheticals, in the genome of M. tuberculosis during the log phase of in vitro growth. The gene expression profiles were analyzed and visualized through cluster analysis to epitomize the full details of genomic behavior. Broad patterns derived from genome-wide expression experiments in this study have provided insight into the interrelationships among genes in the basic cellular processes of M. tuberculosis. CONCLUSION: Our results have confirmed several known gene clusters in energy production, information pathways, and lipid metabolism, and also hinted at potential roles of hypothetical and regulatory proteins.

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Sequencing the complete genome of Mycobacterium tuberculosis H37Rv is a major milestone in the genome project and it sheds new light in our fight with tuberculosis. The genome contains around 4000 genes (protein-coding sequences) in the original genome annotation. A subsequent reannotation of the genome has added 80 more genes. However, we have found that the intergenic regions can exhibit expression signals, as evidenced by microarray hybridization. It is then reasonable to suspect that there are unidentified genes in these regions. We conducted a genome-wide analysis using the Affymetrix GeneChip to explore genes contained in the intergenic sequences of the M. tuberculosis H37Rv genome. A working criterion for potential protein-coding genes was based on bioinformatics, consisting of the gene structure, protein coding potential, and presence of ortholog evidence. The bioinformatics criteria in conjunction with transcriptional evidence revealed potential genes with a specific function, such as a DNA-binding protein in the CopG family and a nickel binding GTPase, as well as hypothetical proteins that had not been reported in the H37Rv genome. This study further demonstrated that microarray-based transcriptional evidence would facilitate genome-wide gene finding, and is also the first report concerning intergenic expression in M. tuberculosis genome.

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The availability of the complete sequence of Mycobacterium tuberculosis genome coupled with microarray technology has enabled a high-throughput approach to the pharmacogenomics of this organism. Isoniazid (INH) is a first-line drug for the treatment of tuberculosis and the microarray approach has generated new insight into the action of INH on a drug-susceptible strain. It has also shown that INH does not induce any significant change in gene expression when applied to a catalase-negative INH-resistant strain, which is expected because catalase activity is required to convert the prodrug INH to its active form. But it has yet to be determined how a partially resistant strain responds to INH. In this study, we explore the mechanism of INH against a highly INH-resistant strain, compare drug-induced gene-expression profiles between resistant and susceptible strains, and determine whether or not and how the resistant strain responds to INH at low and high concentrations. The global gene-expression profiles of the resistant strain in response to INH treatments were obtained using the Affymetrix oligonucleotide GeneChips. The results showed that the resistant strain did not exhibit the characteristic gene-expression signature of type II fatty acid synthase (FAS-II) inhibition when exposed to low-level INH, but it responded with that specific pattern under high-level INH, although the response profile was somewhat shrunken relative to that for a susceptible strain. We found that INH acted on the FAS-II pathway in both resistant and susceptible strains, and little evidence suggested that INH might kill resistant bacteria via other mechanisms. This suggests that there may be potential benefit of treating INH-resistant bacteria with INH at a level that is effective and safe.

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OBJECTIVE: Multi-drug resistance and latent infection are two major issues in current tuberculosis (TB) control and management. Capreomycin is an important drug used for TB with multi-drug resistance. A recent study also indicates that this drug possesses unique bactericidal activity against non-replicating TB bacilli among known anti-TB drugs. Thus, there is an urgent need for investigating the full-spectrum action of capreomycin. METHODS: Here we conduct the first microarray-based study on capreomycin using the high-resolution Affymetrix oligonucleotide GeneChip system. RESULTS: The results indicate that capreomycin primarily acts on the information pathways but it also significantly affects cell wall, cell processes, intermediate metabolism and respiration in Mycobacterium tuberculosis. CONCLUSIONS: This study not only transcriptionally validates the specific molecular target, 16S rRNA, but also discovers potential new targets of capreomycin, including genes operating at the DNA level, such as Rv0054 (ssb) and Rv3715c (recR), as well as genes involved in cell division like Rv3260c (whiB2). In addition, the nuo gene cluster and the ATP synthase gene cluster are repressed.

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DNA microarrays have rapidly emerged as an important tool for Mycobacterium tuberculosis research. While the microarray approach has generated valuable information, a recent survey has found a lack of correlation among the microarray data produced by different laboratories on related issues, raising a concern about the credibility of research findings. The Affymetrix oligonucleotide array has been shown to be more reliable for interrogating changes in gene expression than other platforms. However, this type of array system has not been applied to the pharmacogenomic study of M. tuberculosis. The goal here was to explore the strength of the Affymetrix array system for monitoring drug-induced gene expression in M. tuberculosis, compare with other related studies, and conduct cross-platform analysis. The genome-wide gene expression profiles of M. tuberculosis in response to drug treatments including INH (isoniazid) and ethionamide were obtained using the Affymetrix array system. Up-regulated or down-regulated genes were identified through bioinformatic analysis of the microarray data derived from the hybridization of RNA samples and gene probes. Based on the Affymetrix system, our method identified all drug-induced genes reported in the original reference work as well as some other genes that have not been recognized previously under the same drug treatment. For instance, the Affymetrix system revealed that Rv2524c (fas) was induced by both INH and ethionamide under the given levels of concentration, as suggested by most of the probe sets implementing this gene sequence. This finding is contradictory to previous observations that the expression of fas is not changed by INH treatment. This example illustrates that the determination of expression change for certain genes is probe-dependent, and the appropriate use of multiple probe-set representation is an advantage with the Affymetrix system. Our data also suggest that whereas the up-regulated gene expression pattern reflects the drug's mode of action, the down-regulated pattern is largely non-specific. According to our analysis, the Affymetrix array system is a reliable tool for studying the pharmacogenomics of M. tuberculosis and lends itself well in the research and development of anti-TB drugs.

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BACKGROUND: Microarray devices permit a genome-scale evaluation of gene function. This technology has catalyzed biomedical research and development in recent years. As many important diseases can be traced down to the gene level, a long-standing research problem is to identify specific gene expression patterns linking to metabolic characteristics that contribute to disease development and progression. The microarray approach offers an expedited solution to this problem. However, it has posed a challenging issue to recognize disease-related genes expression patterns embedded in the microarray data. In selecting a small set of biologically significant genes for classifier design, the nature of high data dimensionality inherent in this problem creates substantial amount of uncertainty. RESULTS: Here we present a model for probability analysis of selected genes in order to determine their importance. Our contribution is that we show how to derive the P value of each selected gene in multiple gene selection trials based on different combinations of data samples and how to conduct a reliability analysis accordingly. The importance of a gene is indicated by its associated P value in that a smaller value implies higher information content from information theory. On the microarray data concerning the subtype classification of small round blue cell tumors, we demonstrate that the method is capable of finding the smallest set of genes (19 genes) with optimal classification performance, compared with results reported in the literature. CONCLUSION: In classifier design based on microarray data, the probability value derived from gene selection based on multiple combinations of data samples enables an effective mechanism for reducing the tendency of fitting local data particularities.

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Differential diagnosis among a group of histologically similar cancers poses a challenging problem in clinical medicine. Constructing a classifier based on gene expression signatures comprising multiple discriminatory molecular markers derived from microarray data analysis is an emerging trend for cancer diagnosis. To identify the best genes for classification using a small number of samples relative to the genome size remains the bottleneck of this approach, despite its promise. We have devised a new method of gene selection with reliability analysis, and demonstrated that this method can identify a more compact set of genes than other methods for constructing a classifier with optimum predictive performance for both small round blue cell tumors and leukemia. High consensus between our result and the results produced by methods based on artificial neural networks and statistical techniques confers additional evidence of the validity of our method. This study suggests a way for implementing a reliable molecular cancer classifier based on gene expression signatures.

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UNLABELLED: A Web-based database system was constructed and implemented that contains 174 tumor suppressor genes. The database homepage was created to accommodate these genes in a pull-down window so that each gene can be viewed individually in a separate Web page. Information displayed on each page includes gene name, aliases, source organism, chromosome location, expression cells/tissues, gene structure, protein size, gene functions and major reference sources. Queries to the database can be conducted through a user-friendly interface, and query results are returned in the HTML format on dynamically generated web pages. AVAILABILITY: The database is available at http://www.cise.ufl.edu/~yy1/HTML-TSGDB/Homepage.html (data files also at http://www.patcar.org/Databases/Tumor_Suppressor_Genes)

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Constructing a classifier based on microarray gene expression data has recently emerged as an important problem for cancer classification. Recent results have suggested the feasibility of constructing such a classifier with reasonable predictive accuracy under the circumstance where only a small number of cancer tissue samples of known type are available. Difficulty arises from the fact that each sample contains the expression data of a vast number of genes and these genes may interact with one another. Selection of a small number of critical genes is fundamental to correctly analyze the otherwise overwhelming data. It is essential to use a multivariate approach for capturing the correlated structure in the data. However, the curse of dimensionality leads to the concern about the reliability of selected genes. Here, we present a new gene selection method in which error and repeatability of selected genes are assessed within the context of M-fold cross-validation. In particular, we show that the method is able to identify source variables underlying data generation.

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The availability of the complete genome sequence of Mycobacterium tuberculosis allows its phylogenetic analysis based on the whole genome rather than single genes. As a genome-based tree is more representative of whole organisms and less inconsistent than single-gene trees, it could provide a better index for interpretation and inference about the origin and nature of species. The standard bacterial phylogeny based on 16S ribosomal RNA sequence comparison shows that M. tuberculosis is more related to Gram-positive than to Gram-negative bacteria. Our results based on genome comparison in terms of shared orthologous genes challenge this implication. We demonstrate that M. tuberculosis is more related to Gram-negative than to Gram-positive bacteria by a quantitative analysis on the genome tree. The numerical distance data derived from genome comparison and those from 16S rRNA comparison show high significant correlation, implying that conserved gene content carries a strong phylogenetic signature in evolution.

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