The Cross-Border Biotech Blog

Biotechnology, Health and Business in Canada, the United States and Worldwide

Friday Science Review: May 13, 2011

Environmental Stimuli Enhance Visual System Function

McGill University ♦ Published in Neuron (Cell Press), May 12, 2011

The developing nervous system utilizes sensory inputs to lay down the correct neural circuits, strengthening and weakening specific connections where necessary. Sensory cues from the external environment can play a role in neural development as well.  A new study from McGill illustrates that acute environmental stimuli can have strong and long-lasting effects on both synaptic plasticity and functional refinement during development. Using the Xenopus tadpole as a model to investigate the matter, researchers analyzed the effects that 20 minutes of visual stimulation had on the development of the visual system. They found that visual stimulation increased transcription of brain-derived neurotrophic factor (BDNF), a protein that is involved in refining the nervous system by strengthening appropriate synaptic connections and eliminating those that are inappropriate. The effects of visual conditioning went beyond increasing expression of BDNF in the short-term, it also led to improved visual acuity following the completion of development. Animals that were conditioned with visual stimulation and then returned to their normal rearing environment had improved visual system function, as measured by their overall visual acuity.

Array-based Platforms for CNV Analysis: Establishing a Benchmark

Hospital for Sick Children ♦ University of Toronto ♦ Harvard Medical School ♦ Sanger Institute ♦ Uppsala University ♦ University College of Medicine

Published in Nature Biotechnology, May 8, 2011

The largest component of genomic variation within humans lies in copy number variants (CNVs), segments of DNA that duplicate causing expanded regions in the genome. Differences in this variable genetic content between individuals in part explains the differences that humans have in their susceptibility to disease. The detection of CNVs has been playing an ever-more important role in cancer research, clinical diagnostics, and genome-wide association studies. To date, the scientific community has discovered over 15,000 CNVs in the human genome which have been logged in the Database of Genomic Variants. Despite the known importance that CNVs play in disease pathology, there remain several factors related to CNV detection platforms that have hampered the use of this data in research and clinical settings.

The two primary platforms for CNV detection are comparative genomic hybridization arrays and single nucleotide polymorphism arrays, a number of which have been released over the last several years with a trend toward higher resolution. The absence of standardization in CNV reporting and reference samples makes comparing platforms exceedingly difficult, which is exacerbated by a range of platforms that have different genome coverage and resolution. Another complication are the algorithms used to “call”  or identify CNVs, which themselves can be quite different. There has obviously been a need for a robust comparability study between today’s current platforms, and this recent study is the first that establishes a benchmark for these platforms to live up to.  Researchers carried out a comprehensive evaluation of 11 CNV detection platforms, looking at data quality, CNV calling, reproducibility, concordance across array platforms and laboratory sites, amongst other things.

Each array was used to analyze six well-characterized control samples in triplicate. As would be expected newer arrays outperformed older arrays when it came to the number of CNV calls and the reproducibility of calls; likely due to their higher resolution and the performance of their probes. An important finding to drive home is that the choice of analysis tool can be just as important as the choice of microarray for accurate CNV detection. When using identical raw data, different algorithms gave rise to considerably different call numbers of varying quality. Researchers suggest that customized algorithms be made for individual platforms and specific data types to reduce this variation. Overall, this assessment of array-based platforms should stress the importance of experimental design in CNV discovery and association studies, to ensure that the reliability and consistency of CNV detection platforms is upheld for their future use in the clinic. The authors of this work have made all of their raw data available to the scientific community providing an extraordinarily robust reference set for future analysis.

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