Toxicogenomic and Toxicoproteomic Approaches for Biomarkers
Anthony P. DeCaprio in Toxicologic Biomarkers, 2006
The development of DNA microarray methodologies was clearly a major leap forward in the analysis of differential gene expression. Not only can this approach simultaneously examine large numbers of individual genes—or in some cases the entire expressed genome—but it also provides a means to simultaneously test existing hypotheses of the investigator, as well as generate entirely new hypotheses. The power of this latter capability cannot be underestimated. While much of recent experimental biology has been dominated by hypothesis-driven research—and, in fact, some scientists refer to hypothesis-generating or discovery-driven experiments pejoratively as “fishing expeditions”—the history and foundation of modern empirical biology is fundamentally that of observational science. Indeed, the history of biological research demonstrates that the biological systems we study are far more complex than typical reductionist models that drive hypothesis testing can predict. DNA microarrays provide the ability to globally assess the mRNA expression of a biological system in response to a stimulus without external bias from the investigator as to what the predicted response might be. Moreover, it provides a means to examine patterns of gene expression, rather than, or in addition to, examination of individual genes. Nonetheless, the best genomics and proteomics experiments combine both aspects, i.e., testing of a specific hypothesis and subsequent analysis of novel patterns or individual genes of interest which generate new hypotheses (35).
Encephalitozoon
Dongyou Liu in Handbook of Foodborne Diseases, 2018
The report about the implementation of methods of DNA chips (DNA “microarray”) for the parallel detection of several species of microsporidia (E. cuniculi, E. hellem, and E. intestinalis) in clinical samples75 is very interesting. The great advantage of a DNA “microarray” compared to the PCR method is the ability to diagnose a high number of unknown samples. Unlike PCR, DNA is not obtained through laborious processing of spores by preextraction steps, but by using FTA filters, which not only eliminates these labor-intensive procedures, but also helps avoid the significant loss of DNA and effectively removes inhibitors from fecal samples. Compared to the commercial DNA extraction kits, this method results in lower financial costs, requires less technical training, requires less equipment, and can process a larger number of samples simultaneously.76 The disadvantage of DNA “microarray” is not only expensive laboratory instrumentation, but also the synthesis of large amounts of primers. But once the preparation of the DNA microarray is completed, the actual execution of tests is considerably cheaper. The method of the DNA microarray, as described by Wang et al. (2005),75 represents a combination of the PCR method, which is followed by hybridization of the amplicons using more specific probes immobilized on a microchip. The fluorescence intensity correlates with the abundance of DNA in a sample.
Genetics of chronic pain: crucial concepts in genetics and research tools to understand the molecular biology of pain and analgesia
Peter R Wilson, Paul J Watson, Jennifer A Haythornthwaite, Troels S Jensen in Clinical Pain Management, 2008
DNA microarrays refer to the class of high-throughput technologies that permit the screening of hundreds to hundreds of thousands of variant sequences (e.g. SNPs, copy number variations). DNA from an individual or organism under study is applied to the microarray. Complementary sequences hybridize to their target, which permits simultaneous assay of all of the sequences (i.e. polymorphisms) featured on the microarray. Common uses of DNA microarrays include susceptibility gene discovery and drug development. Recent success in the application of DNA microarray-based gene discovery for migraine suggest that population genetic studies of pain phenotypes are now tenable.31 In addition, the recent release of commercial DNA microarray-based pain candidate gene panels (http://www.congenics.com) represents an intriguing research tool to explore inter-individual variation in pain, analgesia, and allodynia in human populations.
Novel strategies for rapid identification and susceptibility testing of MRSA
Published in Expert Review of Anti-infective Therapy, 2020
Masako Mizusawa, Karen C Carroll
The commercially available molecular-based assays for detection of MRSA in blood cultures are categorized into four groups: 1) multiplex or monoplex nucleic acid amplification-based assays, 2) an in situ hybridization-based assay, 3) a DNA microarray-based assay, and 4) a combination of multiplex nucleic acid amplification and in situ hybridization. Most of the multiplex assays allow blood culture specimens to be tested within 8 h after the blood culture bottles are identified as positive by a continuous monitoring blood culture system. A number of studies on the utility of rapid molecular-based diagnostic tests for blood cultures demonstrated the positive impact on clinical outcomes such as time to effective/appropriate antibiotic therapy, length of hospital stay, and associated costs, but mostly when combined with antimicrobial stewardship interventions [116]. The analytic performance of the FDA-cleared and CE-marked assays for MRSA detection in blood cultures is summarized in Table 2.
Could a blood test for PTSD and depression be on the horizon?
Published in Expert Review of Proteomics, 2018
Dario Aspesi, Graziano Pinna
In addition, gene-activity assay is a promising technique and cost-efficient; however, this technology requires more investigation to identify specific genes that change their expression in PTSD and MDD [88]. DNA microarrays can efficiently highlight gene expression profiling of transcriptional reactivity. Currently, studies that analyze gene expression related to PTSD showed relevant signatures in mononuclear cells that may be useful to diagnose a mental disorder [272]. However, improved methods are still required to screen more efficiently through sets of candidate variants, and then, a rigorous validation of variants and gene effects are also needed [273]. Furthermore, replications in larger samples and investigations focusing on selected markers as part of the biosignatures that have been discovered, are required to assess the diagnostic utility and pathological relevance of these methods.
Remaining challenges in predicting patient outcomes for diffuse large B-cell lymphoma
Published in Expert Review of Hematology, 2019
R. Andrew Harkins, Andres Chang, Sharvil P. Patel, Michelle J. Lee, Jordan S. Goldstein, Selin Merdan, Christopher R. Flowers, Jean L. Koff
Utilization of DNA microarray and, more recently, high-throughput sequencing technologies have spawned large amounts of gene expression data. Integration of gene expression data with clinical, histological, imaging, demographic, and epidemiological information could provide insights for improving cancer diagnosis and prognosis. However, the enormity and complexity of data obtained from cancer-related gene expression studies present great challenges in making accurate predictions of clinical outcomes. Machine learning methods are designed to organize, process, and discover actionable knowledge in high-dimensional settings. As such, several different types of machine learning methods have been adapted to achieve three fundamental predictive tasks in cancer research: 1) prediction of cancer susceptibility (risk assessment); 2) prediction of cancer recurrence; and 3) prediction of cancer survival outcomes [89,90]. An important challenge in translating high-dimensional data into accurate predictions for clinical decision-making is to identify informative features (e.g., clinical risk factors and genes) that contribute most to the prediction. Firstly, a more compact model will be more useful and interpretable in predicting outcomes for future patients. Secondly, selecting informative features is critical to avoiding overfitting and improving the accuracy and speed of prediction systems. Lastly, informative features allow investigators to understand the underlying cancer mechanisms that generated the data.
Related Knowledge Centers
- Biochip
- Complementary DNA
- DNA
- Fluorophore
- Gene Expression
- Genotyping
- Microarray
- Oligonucleotide
- Hybridization Probe
- Gene