Novel Approaches to Gene Expression Analysis
Novel Approaches to Gene Expression Analysis
Juvenile rheumatoid arthritis (JRA) has a complex, poorly characterized pathophysiology. Modeling of transcriptosome behavior in pathologic specimens using microarrays allows molecular dissection of complex autoimmune diseases. However, conventional analyses rely on identifying statistically significant differences in gene expression distributions between patients and controls. Since the principal aspects of disease pathophysiology vary significantly among patients, these analyses are biased. Genes with highly variable expression, those most likely to regulate and affect pathologic processes, are excluded from selection, as their distribution among healthy and affected individuals may overlap significantly. Here we describe a novel method for analyzing microarray data that assesses statistically significant changes in gene behavior at the population level. This method was applied to expression profiles of peripheral blood leukocytes from a group of children with polyarticular JRA and healthy control subjects. Results from this method are compared with those from a conventional analysis of differential gene expression and shown to identify discrete subsets of functionally related genes relevant to disease pathophysiology. These results reveal the complex action of the innate and adaptive immune responses in patients and specifically underscore the role of IFN- γ in disease pathophysiology. Discriminant function analysis of data from a cohort of patients treated with conventional therapy identified additional subsets of functionally related genes; the results may predict treatment outcomes. While data from only 9 patients and 12 healthy controls was used, this preliminary investigation of the inflammatory genomics of JRA illustrates the significant potential of utilizing complementary sets of bioinformatics tools to maximize the clinical relevance of microarray data from patients with autoimmune disease, even in small cohorts.
'Juvenile rheumatoid arthritis' (JRA), a term for the most prevalent form of arthritis in children, is applied to a family of illnesses characterized by chronic inflammation and hypertrophy of the synovial membranes. The term overlaps, but is not completely synonymous, with the family of illnesses referred to as juvenile chronic arthritis and/or juvenile idiopathic arthritis in Europe. We and others have proposed that the pathogenesis of rheumatoid disease in adults and children involves complex interactions between innate and adaptive immunity. This complexity lies at the core of the difficulty of unraveling disease pathogenesis. Both innate and adaptive immune systems use multiple cell types, a vast array of cell-surface and secreted proteins, and interconnected networks of positive and negative feedback. Furthermore, while separable in thought, the innate and adaptive wings of the immune system are functionally intersected, and pathologic events occurring at these intersecting points are likely to be highly relevant to our understanding of pathogenesis of adult and childhood forms of chronic arthritis.
Polyarticular JRA is a distinct clinical subtype characterized by inflammation and synovial proliferation in multiple joints (four or more), including the small joints of the hands. This subtype of JRA may be severe, because of both its multiple joint involvement and its capacity to progress rapidly over time. Although clinically distinct, polyarticular JRA is not homogeneous, and patients vary in disease manifestations, age of onset, prognosis, and therapeutic response. These differences very likely reflect a spectrum of variation in the nature of the immune and inflammatory attack that can occur in this disease.
Gene expression profiling using microarrays provides a highly parallel assay for assessing molecular pathophysiology in a comprehensive manner. It holds the potential to refine our understanding of complex disease states. However, microarray data analysis is commonly limited to a simple assessment of a single behavioral change in gene expression, genes that are up- or down-regulated on average among distinct populations. This approach has been used to identify groups of genes that are prognostically or diagnostically relevant, but the predictive power of these gene sets for autoimmune disease has proved limited. Changes in gene behavior among individuals in diseased populations are complex and may reflect both the unique genetic makeup of individuals and distinct subclasses of disease.
In this preliminary investigation of the inflammatory genomics of JRA, we report the application of a novel bioinformatics approach to microarray data for the identification of genes whose expression behavior is modulated by disease in a complex manner at the population level. Accordingly, genes whose expression within a population changes from stable to variable are identified. This measure of gene behavior emulates at the molecular level the loss of homeostasis characteristic of disease pathogenesis. The method identified a significant number of genes relevant to the pathophysiology of polyarticular JRA distinct from those identified by standard differential gene expression analysis. In addition, we followed a subset of patients during therapy to characterize temporally dependent changes in gene expression. Using discriminant function analysis (DFA) to analyze this cohort, we identified gene expression changes characteristic of therapeutic response approximately one month before the time at which full clinical response occurred. A clinical assay could be created from this data that may predict soon after initiation of therapy which patients will respond and which will not. The predictive potential of this data is predicated on the fact that within 2 to 4 weeks after the start of therapy, gene expression in responsive patients, as measured by DFA, became more like that in healthy controls, while gene expression in nonresponsive patients became less like that in healthy controls. Moreover, the genes identified by DFA to be predictive of therapeutic response were, for the most part, known regulators and effectors of the immune system. Taken together, these data suggest that successful therapy was able to reset immune response homeostasis to a significant extent in this cohort.
Juvenile rheumatoid arthritis (JRA) has a complex, poorly characterized pathophysiology. Modeling of transcriptosome behavior in pathologic specimens using microarrays allows molecular dissection of complex autoimmune diseases. However, conventional analyses rely on identifying statistically significant differences in gene expression distributions between patients and controls. Since the principal aspects of disease pathophysiology vary significantly among patients, these analyses are biased. Genes with highly variable expression, those most likely to regulate and affect pathologic processes, are excluded from selection, as their distribution among healthy and affected individuals may overlap significantly. Here we describe a novel method for analyzing microarray data that assesses statistically significant changes in gene behavior at the population level. This method was applied to expression profiles of peripheral blood leukocytes from a group of children with polyarticular JRA and healthy control subjects. Results from this method are compared with those from a conventional analysis of differential gene expression and shown to identify discrete subsets of functionally related genes relevant to disease pathophysiology. These results reveal the complex action of the innate and adaptive immune responses in patients and specifically underscore the role of IFN- γ in disease pathophysiology. Discriminant function analysis of data from a cohort of patients treated with conventional therapy identified additional subsets of functionally related genes; the results may predict treatment outcomes. While data from only 9 patients and 12 healthy controls was used, this preliminary investigation of the inflammatory genomics of JRA illustrates the significant potential of utilizing complementary sets of bioinformatics tools to maximize the clinical relevance of microarray data from patients with autoimmune disease, even in small cohorts.
'Juvenile rheumatoid arthritis' (JRA), a term for the most prevalent form of arthritis in children, is applied to a family of illnesses characterized by chronic inflammation and hypertrophy of the synovial membranes. The term overlaps, but is not completely synonymous, with the family of illnesses referred to as juvenile chronic arthritis and/or juvenile idiopathic arthritis in Europe. We and others have proposed that the pathogenesis of rheumatoid disease in adults and children involves complex interactions between innate and adaptive immunity. This complexity lies at the core of the difficulty of unraveling disease pathogenesis. Both innate and adaptive immune systems use multiple cell types, a vast array of cell-surface and secreted proteins, and interconnected networks of positive and negative feedback. Furthermore, while separable in thought, the innate and adaptive wings of the immune system are functionally intersected, and pathologic events occurring at these intersecting points are likely to be highly relevant to our understanding of pathogenesis of adult and childhood forms of chronic arthritis.
Polyarticular JRA is a distinct clinical subtype characterized by inflammation and synovial proliferation in multiple joints (four or more), including the small joints of the hands. This subtype of JRA may be severe, because of both its multiple joint involvement and its capacity to progress rapidly over time. Although clinically distinct, polyarticular JRA is not homogeneous, and patients vary in disease manifestations, age of onset, prognosis, and therapeutic response. These differences very likely reflect a spectrum of variation in the nature of the immune and inflammatory attack that can occur in this disease.
Gene expression profiling using microarrays provides a highly parallel assay for assessing molecular pathophysiology in a comprehensive manner. It holds the potential to refine our understanding of complex disease states. However, microarray data analysis is commonly limited to a simple assessment of a single behavioral change in gene expression, genes that are up- or down-regulated on average among distinct populations. This approach has been used to identify groups of genes that are prognostically or diagnostically relevant, but the predictive power of these gene sets for autoimmune disease has proved limited. Changes in gene behavior among individuals in diseased populations are complex and may reflect both the unique genetic makeup of individuals and distinct subclasses of disease.
In this preliminary investigation of the inflammatory genomics of JRA, we report the application of a novel bioinformatics approach to microarray data for the identification of genes whose expression behavior is modulated by disease in a complex manner at the population level. Accordingly, genes whose expression within a population changes from stable to variable are identified. This measure of gene behavior emulates at the molecular level the loss of homeostasis characteristic of disease pathogenesis. The method identified a significant number of genes relevant to the pathophysiology of polyarticular JRA distinct from those identified by standard differential gene expression analysis. In addition, we followed a subset of patients during therapy to characterize temporally dependent changes in gene expression. Using discriminant function analysis (DFA) to analyze this cohort, we identified gene expression changes characteristic of therapeutic response approximately one month before the time at which full clinical response occurred. A clinical assay could be created from this data that may predict soon after initiation of therapy which patients will respond and which will not. The predictive potential of this data is predicated on the fact that within 2 to 4 weeks after the start of therapy, gene expression in responsive patients, as measured by DFA, became more like that in healthy controls, while gene expression in nonresponsive patients became less like that in healthy controls. Moreover, the genes identified by DFA to be predictive of therapeutic response were, for the most part, known regulators and effectors of the immune system. Taken together, these data suggest that successful therapy was able to reset immune response homeostasis to a significant extent in this cohort.