Biomedical subjects

P G McQueen

Publications and source records attributed to P G McQueen.

3 recordsLinked to original sources

PubMed2004

Spatial genome organization during T-cell differentiation.

The spatial organization of genomes within the mammalian cell nucleus is non-random. The functional relevance of spatial genome organization might be in influencing gene expression programs as cells undergo changes during development and differentiation. To gain insight into the plasticity of genomes in space and time and to correlate the activity of specific genes with their nuclear position, we systematically analyzed the spatial genome organization in differentiating mouse T-cells. We find significant global reorganization of centromeres, chromosomes and gene loci during the differentiation process. Centromeres were repositioned from a preferentially internal distribution in undifferentiated cells to a preferentially peripheral position in differentiated CD4+ and CD8+ cells. Chromosome 6, containing the differentially expressed T-cell markers CD4 and CD8, underwent differential changes in position depending on whether cells differentiated into CD4+ or CD8+ thymocytes. Similarly, the two marker loci CD4 and CD8 showed distinct behavior in their position relative to the chromosome 6 centromere at various stages of differentiation. Our results demonstrate that significant spatial genome reorganization occurs during differentiation and indicate that the relationship between dynamic genome topology and single gene regulation is highly complex.

Animals↗

PubMed1999

Comparison of viral load and human leukocyte antigen statistical and neural network predictive models for the rate of HIV-1 disease progression across two cohorts of homosexual men.

We compared the performance of HIV-1 RNA and models based on human leukocyte antigen (HLA) in predicting the rate of HIV-1 disease progression using both linear regression and neural network models across two different cohorts of homosexual men. In all, 139 seroconverters from the Multicenter AIDS Cohort Study were used as the training set and 97 seroconverters from the District of Columbia Gay (DCG) cohort were used for validation to assess the generalizability of trained predictive models. Both viral load and HLA markers were strongly predictive of disease progression (p < .0001 and p = .001, respectively), with viral load superior to HLA (change in -2 log likelihood [-2LL] 26.7 and 10.2, respectively, in proportional hazards models). Consideration of both HLA markers and viral load offered no significant predictive advantage over viral load alone in most cases; however, HLA-based predictions obtained from neural networks modeling improved the discrimination among patients with high viral load (p = .02). Viral load, HLA scores, and rapid disease progression were moderately correlated (p < .01 for all three pairs of these variables). The median viral load was 10(3.70) copies/ml among DCG patients who had more favorable than unfavorable HLA markers and 10(4.66) copies/ml among patients with more unfavorable than favorable HLA markers. Viral load is a simpler, stronger predictor of disease progression than early developed HLA models, but neural network methods and further refined HLA models may offer additional prognostic information, especially for rapid progressors. The correlation between viral load and HLA markers suggests a possible HLA effect on setting viral load levels.

Cohort Studies↗

PubMed1998

Use of neural networks to model complex immunogenetic associations of disease: human leukocyte antigen impact on the progression of human immunodeficiency virus infection.

Complex immunogenetic associations of disease involving a large number of gene products are difficult to evaluate with traditional statistical methods and may require complex modeling. The authors evaluated the performance of feed-forward backpropagation neural networks in predicting rapid progression to acquired immunodeficiency syndrome (AIDS) for patients with human immunodeficiency virus (HIV) infection on the basis of major histocompatibility complex variables. Networks were trained on data from patients from the Multicenter AIDS Cohort Study (n = 139) and then validated on patients from the DC Gay cohort (n = 102). The outcome of interest was rapid disease progression, defined as progression to AIDS in <6 years from seroconversion. Human leukocyte antigen (HLA) variables were selected as network inputs with multivariate regression and a previously described algorithm selecting markers with extreme point estimates for progression risk. Network performance was compared with that of logistic regression. Networks with 15 HLA inputs and a single hidden layer of five nodes achieved a sensitivity of 87.5% and specificity of 95.6% in the training set, vs. 77.0% and 76.9%, respectively, achieved by logistic regression. When validated on the DC Gay cohort, networks averaged a sensitivity of 59.1% and specificity of 74.3%, vs. 53.1% and 61.4%, respectively, for logistic regression. Neural networks offer further support to the notion that HIV disease progression may be dependent on complex interactions between different class I and class II alleles and transporters associated with antigen processing variants. The effect in the current models is of moderate magnitude, and more data as well as other host and pathogen variables may need to be considered to improve the performance of the models. Artificial intelligence methods may complement linear statistical methods for evaluating immunogenetic associations of disease.

Acquired Immunodeficiency Syndrome↗