Methodology and software to detect viral integration site hot-spots

<p>Abstract</p> <p>Background</p> <p>Modern gene therapy methods have limited control over where a therapeutic viral vector inserts into the host genome. Vector integration can activate local gene expression, which can cause cancer if the vector inserts near an oncogene. Viral integration hot-spots or 'common insertion sites' (CIS) are scrutinized to evaluate and predict patient safety. CIS are typically defined by a minimum density of insertions (such as 2-4 within a 30-100 kb region), which unfortunately depends on the total number of observed VIS. This is problematic for comparing hot-spot distributions across data sets and patients, where the VIS numbers may vary.</p> <p>Results</p> <p>We develop two new methods for defining hot-spots that are relatively independent of data set size. Both methods operate on distributions of VIS across consecutive 1 Mb 'bins' of the genome. The first method 'z-threshold' tallies the number of VIS per bin, converts these counts to z-scores, and applies a threshold to define high density bins. The second method 'BCP' applies a Bayesian change-point model to the z-scores to define hot-spots. The novel hot-spot methods are compared with a conventional CIS method using simulated data sets and data sets from five published human studies, including the X-linked ALD (adrenoleukodystrophy), CGD (chronic granulomatous disease) and SCID-X1 (X-linked severe combined immunodeficiency) trials. The BCP analysis of the human X-linked ALD data for two patients separately (774 and 1627 VIS) and combined (2401 VIS) resulted in 5-6 hot-spots covering 0.17-0.251% of the genome and containing 5.56-7.74% of the total VIS. In comparison, the CIS analysis resulted in 12-110 hot-spots covering 0.018-0.246% of the genome and containing 5.81-22.7% of the VIS, corresponding to a greater number of hot-spots as the data set size increased. Our hot-spot methods enable one to evaluate the extent of VIS clustering, and formally compare data sets in terms of hot-spot overlap. Finally, we show that the BCP hot-spots from the repopulating samples coincide with greater gene and CpG island density than the median genome density.</p> <p>Conclusions</p> <p>The z-threshold and BCP methods are useful for comparing hot-spot patterns across data sets of disparate sizes. The methodology and software provided here should enable one to study hot-spot conservation across a variety of VIS data sets and evaluate vector safety for gene therapy trials.</p

Heterogeneity of Glia in the Retina and Optic Nerve of Birds and Mammals

We have recently described a novel type of glial cell that is scattered across the inner layers of the avian retina [1]. These cells are stimulated by insulin-like growth factor 1 (IGF1) to proliferate, migrate distally into the retina, and up-regulate the nestin-related intermediate filament transitin. These changes in glial activity correspond with increased susceptibility of neurons to excitotoxic damage. This novel cell-type has been termed the Non-astrocytic Inner Retinal Glia-like (NIRG) cells. The purpose of the study was to investigate whether the retinas of non-avian species contain cells that resemble NIRG cells. We assayed for NIRG cells by probing for the expression of Sox2, Sox9, Nkx2.2, vimentin and nestin. NIRG cells were distinguished from astrocytes by a lack of expression for Glial Fibrilliary Acidic Protein (GFAP). We examined the retinas of adult mice, guinea pigs, dogs and monkeys (Macaca fasicularis). In the mouse retina and optic nerve head, we identified numerous astrocytes that expressed GFAP, S100β, Sox2 and Sox9; however, we found no evidence for NIRG-like cells that were positive for Nkx2.2, nestin, and negative for GFAP. In the guinea pig retina, we did not find astrocytes or NIRG cells in the retina, whereas we identified astrocytes in the optic nerve. In the eyes of dogs and monkeys, we found astrocytes and NIRG-like cells scattered across inner layers of the retina and within the optic nerve. We conclude that NIRG-like cells are present in the retinas of canines and non-human primates, whereas the retinas of mice and guinea pigs do not contain NIRG cells

Multifractal Spatial Patterns and Diversity in an Ecological Succession

We analyzed the relationship between biodiversity and spatial biomass heterogeneity along an ecological succession developed in the laboratory. Periphyton (attached microalgae) biomass spatial patterns at several successional stages were obtained using digital image analysis and at the same time we estimated the species composition and abundance. We show that the spatial pattern was self-similar and as the community developed in an homogeneous environment the pattern is self-organized. To characterize it we estimated the multifractal spectrum of generalized dimensions Dq. Using Dq we analyze the existence of cycles of heterogeneity during succession and the use of the information dimension D1 as an index of successional stage. We did not find cycles but the values of D1 showed an increasing trend as the succession developed and the biomass was higher. D1 was also negatively correlated with Shannon's diversity. Several studies have found this relationship in different ecosystems but here we prove that the community self-organizes and generates its own spatial heterogeneity influencing diversity. If this is confirmed with more experimental and theoretical evidence D1 could be used as an index, easily calculated from remote sensing data, to detect high or low diversity areas

ICAR: endoscopic skull‐base surgery

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