Martin J. Firth

Gene expression levels assessed by oligonucleotide microarray analysis and quantitative real-time RT-PCR -- how well do they correlate?

BackgroundThe use of microarray technology to assess gene expression levels is now widespread in biology. The validation of microarray results using independent mRNA quantitation techniques remains a desirable element of any microarray experiment. To facilitate the comparison of microarray expression data between laboratories it is essential that validation methodologies be critically examined. We have assessed the correlation between expression scores obtained for 48 human genes using oligonucleotide microarrays and the expression levels for the same genes measured by quantitative real-time RT-PCR (qRT-PCR).ResultsCorrelations with qRT-PCR data were obtained using microarray data that were processed using robust multi-array analysis (RMA) and the MAS 5.0 algorithm. Our results indicate that when identical transcripts are targeted by the two methods, correlations between qRT-PCR and microarray data are generally strong (r = 0.89). However, we observed poor correlations between qRT-PCR and RMA or MAS 5.0 normalized microarray data for 13% or 16% of genes, respectively.ConclusionThese results highlight the complementarity of oligonucleotide microarray and qRT-PCR technologies for validation of gene expression measurements, while emphasizing the continuing requirement for caution in interpreting gene expression data.

The gene expression signature of relapse in paediatric acute lymphoblastic leukaemia: implications for mechanisms of therapy failure

Despite significant improvements in the treatment of childhood acute lymphoblastic leukaemia (ALL), the prognosis for relapsing patients remains poor. The aim of this study was to generate a transcriptional profile of relapsed ALL to increase our understanding of the mechanisms involved in therapy failure. RNA was extracted from 11 pairs of cryopreserved pre‐B ALL bone marrow specimens taken from the same patients at diagnosis and relapse, and analysed using HG‐U133A microarrays. Relapse specimens overexpressed genes that are involved with cell growth and proliferation, in keeping with their aggressive phenotype. When tested in 72 independent specimens of pre‐B ALL and T‐ALL, the identified genes could successfully differentiate between diagnosis and relapse in either lineage, indicating the existence of relapse mechanisms common to both. These genes have functions relevant for oncogenesis, drug resistance and metastasis, but are not related to classical multidrug‐resistance pathways. Increased expression of the top‐ranked gene (BSG) at diagnosis was significantly associated with adverse outcome. Several chromosomal loci, including 19p13, were identified as potential hotspots for aberrant gene expression in relapsed ALL. Our results provide evidence for a link between drug resistance and the microenvironment that has previously only been considered in the context of solid tumour biology.

Altered glucose metabolism in childhood pre-B acute lymphoblastic leukaemia.

The cells of solid tumours are known to have an altered metabolism, with high rates of glucose uptake and glycolysis, which results in the excessive production of lactate. To date there has been no definitive research documenting metabolic changes in acute lymphoblastic leukaemia (ALL) cells. In order to investigate whether ALL cells have an altered metabolism, we initially compared the transcriptional profiles of 22 specimens from paediatric patients diagnosed with ALL to five CD34+ specimens isolated from bone marrow, which was verified in an independent cohort of 101 specimens. Profiling revealed the upregulation of genes facilitating glycolysis in the ALL specimens compared to the CD34+ specimens, while those involved in the tricarboxylic acid cycle were downregulated. Functional studies supported the microarray findings threefold: (1) higher expression of the glucose transport protein glucose transporter 1 in ALL compared to CD34+ specimens, (2) the excessive production of lactate in ALL cell lines and (3) sensitivity of ALL cell lines to the glycolysis inhibitor 2-deoxy-D-glucose. While metabolic alterations have been well documented in solid tumours, this is the first study to provide direct evidence for the existence of metabolic changes in the leukaemic cells of ALL patients. The finding offers new options for targeted therapy for ALL patients.

Glucocorticoid resistance in T-lineage acute lymphoblastic leukaemia is associated with a proliferative metabolism

Glucocorticoids (GCs) are among the most important drugs for acute lymphoblastic leukaemia (ALL), yet despite their clinical importance, the exact mechanisms involved in GC cytotoxicity and the development of resistance remain uncertain. We examined the baseline profile of a panel of T-ALL cell lines to determine factors that contribute to GC resistance without prior drug selection. Transcriptional profiling indicated GC resistance in T-ALL is associated with a proliferative phenotype involving upregulation of glycolysis, oxidative phosphorylation, cholesterol biosynthesis and glutamate metabolism, increased growth rates and activation of PI3K/AKT/mTOR and MYC signalling pathways. Importantly, the presence of these transcriptional signatures in primary ALL specimens significantly predicted patient outcome. We conclude that in lymphocytes the activation of bioenergetic pathways required for proliferation may suppress the apoptotic potential and offset the metabolic crisis initiated by GC signalling. It is likely that the link between GC resistance and proliferation in T-ALL has not been fully appreciated to date because such effects would be masked in the context of current multiagent therapies. The data also provide the first evidence that altered expression of wild-type MLL may contribute to GC-resistant phenotypes. Our findings warrant the continued development of selective metabolic inhibitors for the treatment of ALL.

Hepatic Iron Loading in Mice Increases Cholesterol Biosynthesis

Iron and cholesterol are both essential metabolites in mammalian systems, and too much or too little of either can have serious clinical consequences. In addition, both have been associated with steatosis and its progression, contributing, inter alia, to an increase in hepatic oxidative stress. The interaction between iron and cholesterol is unclear, with no consistent evidence emerging with respect to changes in plasma cholesterol on the basis of iron status. We sought to clarify the role of iron in lipid metabolism by studying the effects of iron status on hepatic cholesterol synthesis in mice with differing iron status. Transcripts of seven enzymes in the cholesterol biosynthesis pathway were significantly up‐regulated with increasing hepatic iron (R2 between 0.602 and 0.164), including those of the rate‐limiting enzyme, 3‐hydroxy‐3‐methylglutarate‐coenzyme A reductase (Hmgcr; R2 = 0.362, P < 0.002). Hepatic cholesterol content correlated positively with hepatic iron (R2 = 0.255, P < 0.007). There was no significant relationship between plasma cholesterol and either hepatic cholesterol or iron (R2 = 0.101 and 0.014, respectively). Hepatic iron did not correlate with a number of known regulators of cholesterol synthesis, including sterol‐regulatory element binding factor 2 (Srebf2; R2 = 0.015), suggesting that the increases seen in the cholesterol biosynthesis pathway are independent of Srebf2. Transcripts of genes involved in bile acid synthesis, transport, or regulation did not increase with increasing hepatic iron. Conclusion: This study suggests that hepatic iron loading increases liver cholesterol synthesis and provides a new and potentially important additional mechanism by which iron could contribute to the development of fatty liver disease or lipotoxicity. (HEPATOLOGY 2010;)

Identification of Novel Th2-Associated Genes in T Memory Responses to Allergens

Atopic diseases are associated with hyperexpression of Th2 cytokines by allergen-specific T memory cells. However, clinical trials with recently developed Th2 inhibitors in atopics have proven disappointing, suggesting underlying complexities in atopy pathogenesis which are not satisfactorily explained via the classical Th1/Th2 paradigm. One likely possibility is that additional Th2-associated genes which are central to disease pathogenesis remain unidentified. The aim of the present study was to identify such novel Th2-associated genes in recall responses to the inhalant allergen house dust mite. In contrast to earlier human microarray studies in atopy which focused on mitogen-activated T cell lines and clones, we concentrated on PBMC-derived primary T cells stimulated under more physiological conditions of low dose allergen exposure. We screened initially for allergen-induced gene activation by microarray, and validated novel genes in independent panels of subjects by quantitative RT-PCR. Kinetic analysis of allergen responses in PBMC revealed an early wave of novel atopy-associated genes involved in signaling which were coexpressed with IL-4 and IL-4R, followed by a later wave of genes encoding the classical Th2 effector cytokines. We further demonstrate that these novel activation-associated Th2 genes up-regulate in response to another atopy-associated physiological stimulus bacterial superantigen, but remain quiescent in nonphysiological responses in primary T cells or cell lines driven by potent mitogens, which may account for their failure to be detected in earlier microarray studies.

Translating microarray data for diagnostic testing in childhood leukaemia

BackgroundRecent findings from microarray studies have raised the prospect of a standardized diagnostic gene expression platform to enhance accurate diagnosis and risk stratification in paediatric acute lymphoblastic leukaemia (ALL). However, the robustness as well as the format for such a diagnostic test remains to be determined. As a step towards clinical application of these findings, we have systematically analyzed a published ALL microarray data set using Robust Multi-array Analysis (RMA) and Random Forest (RF).MethodsWe examined published microarray data from 104 ALL patients specimens, that represent six different subgroups defined by cytogenetic features and immunophenotypes. Using the decision-tree based supervised learning algorithm Random Forest (RF), we determined a small set of genes for optimal subgroup distinction and subsequently validated their predictive power in an independent patient cohort.ResultsWe achieved very high overall ALL subgroup prediction accuracies of about 98%, and were able to verify the robustness of these genes in an independent panel of 68 specimens obtained from a different institution and processed in a different laboratory. Our study established that the selection of discriminating genes is strongly dependent on the analysis method. This may have profound implications for clinical use, particularly when the classifier is reduced to a small set of genes. We have demonstrated that as few as 26 genes yield accurate class prediction and importantly, almost 70% of these genes have not been previously identified as essential for class distinction of the six ALL subgroups.ConclusionOur finding supports the feasibility of qRT-PCR technology for standardized diagnostic testing in paediatric ALL and should, in conjunction with conventional cytogenetics lead to a more accurate classification of the disease. In addition, we have demonstrated that microarray findings from one study can be confirmed in an independent study, using an entirely independent patient cohort and with microarray experiments being performed by a different research team.

Epidemiological studies of the non-specific effects of vaccines : II - methodological issues in the design and analysis of cohort studies

We review sources of bias which can affect non‐randomized cohort studies of non‐specific effects of vaccines on child mortality. Using examples from the literature on non‐specific effects, we describe different sources of selection and information bias, and, where possible, outline analysis strategies to mitigate or eliminate such biases.

A Network Modeling Approach to Analysis of the Th2 Memory Responses Underlying Human Atopic Disease

Complex cellular functions within immunoinflammatory cascades are conducted by networks of interacting genes. In this study, we employed a network modeling approach to dissect and interpret global gene expression patterns in allergen-induced Th cell responses that underpin human atopic disease. We demonstrate that a subnet of interconnected genes enriched for Th2 and regulatory T cell-associated signatures plus many novel genes is hardwired into the atopic response and is a hallmark of atopy at the systems level. We show that activation of this subnet is stabilized via hyperconnected “hub” genes, the selective disruption of which can collapse the entire network in a comprehensive fashion. Finally, we investigated gene expression in different Th cell subsets and show that regulatory T cell- and Th2-associated signatures partition at different stages of Th memory cell differentiation. Moreover, we demonstrate the parallel presence of a core element of the Th2-associated gene signature in bystander naive cells, which can be reproduced by rIL-4. These findings indicate that network analysis provides significant additional insight into atopic mechanisms beyond that achievable with conventional microarray analyses, predicting functional interactions between novel genes and previously recognized members of the allergic cascade. This approach provides novel opportunities for design of therapeutic strategies that target entire networks of genes rather than individual effector molecules.

High expression of connective tissue growth factor in pre-B acute lymphoblastic leukaemia

In recent years microarrays have been used extensively to characterize gene expression in acute lymphoblastic leukaemia (ALL). Few studies, however, have analysed normal haematopoietic cell populations to identify altered gene expression in ALL. We used oligonucleotide microarrays to compare the gene expression profile of paediatric precursor‐B (pre‐B) ALL specimens with two control cell populations, normal CD34+ and CD19+IgM− cells, to focus on genes linked to leukemogenesis. A set of eight genes was identified with a ninefold higher average expression in ALL specimens compared with control cells. All of these genes were significantly deregulated in an independent cohort of 101 ALL specimens. One gene, connective tissue growth factor (CTGF, also known as CCN2), had exceptionally high expression, which was confirmed in three independent leukaemia studies. Further analysis of CTGF expression in ALL revealed exclusive expression in B‐lineage, not T‐lineage, ALL. Within B‐lineage ALL approximately 75% of specimens were consistently positive for CTGF expression, however, specimens containing the E2A‐PBX1 translocation showed low or no expression. Protein studies using Western blot analysis demonstrated the presence of CTGF in ALL cell‐conditioned media. These findings indicate that CTGF is secreted by pre‐B ALL cells and may play a role in the pathophysiology of this disease.