Test–retest stability of patient experience items derived from the national GP patient survey

PURPOSE: The validity and reliability of various items on the GP Patient Survey (GPPS) survey have been reported, however stability of patient responses over time has not been tested. The purpose of this study was to determine the test-retest reliability of the core items from the GPPS. METHODS: Patients who had recently consulted participating GPs in five general practices across the South West England were sent a postal questionnaire comprising of 54 items concerning their experience of their consultation and the care they received from the GP practice. Patients returning the questionnaire within 3 weeks of mail-out were sent a second identical (retest) questionnaire. Stability of responses was assessed by raw agreement rates and Cohen's kappa (for categorical response items) and intraclass correlation coefficients and means (for ordinal response items). RESULTS: 348 of 597 Patients returned a retest questionnaire (58.3 % response rate). In comparison to the test phase, patients responding to the retest phase were older and more likely to have white British ethnicity. Raw agreement rates for the 33 categorical items ranged from 66 to 100 % (mean 88 %) while the kappa coefficients ranged from 0.00 to 1.00 (mean 0.53). Intraclass correlation coefficients for the 21 ordinal items averaged 0.67 (range 0.44-0.77). CONCLUSIONS: Formal testing of items from the national GP patient survey examining patient experience in primary care highlighted their acceptable temporal stability several weeks following a GP consultation.Funding was provided by Health Services and Delivery Research Programme (Grant No. RP-PG-0608-10050)

Rational Design of Temperature-Sensitive Alleles Using Computational Structure Prediction

Temperature-sensitive (ts) mutations are mutations that exhibit a mutant phenotype at high or low temperatures and a wild-type phenotype at normal temperature. Temperature-sensitive mutants are valuable tools for geneticists, particularly in the study of essential genes. However, finding ts mutations typically relies on generating and screening many thousands of mutations, which is an expensive and labor-intensive process. Here we describe an in silico method that uses Rosetta and machine learning techniques to predict a highly accurate “top 5” list of ts mutations given the structure of a protein of interest. Rosetta is a protein structure prediction and design code, used here to model and score how proteins accommodate point mutations with side-chain and backbone movements. We show that integrating Rosetta relax-derived features with sequence-based features results in accurate temperature-sensitive mutation predictions

Distinct regulation of c-myb gene expression by HoxA9, Meis1 and Pbx proteins in normal hematopoietic progenitors and transformed myeloid cells

The proto-oncogenic protein c-Myb is an essential regulator of hematopoiesis and is frequently deregulated in hematological diseases such as lymphoma and leukemia. To gain insight into the mechanisms underlying the aberrant expression of c-Myb in myeloid leukemia, we analyzed and compared c-myb gene transcriptional regulation using two cell lines modeling normal hematopoietic progenitor cells (HPCs) and transformed myelomonocytic blasts. We report that the transcription factors HoxA9, Meis1, Pbx1 and Pbx2 bind in vivo to the c-myb locus and maintain its expression through different mechanisms in HPCs and leukemic cells. Our analysis also points to a critical role for Pbx2 in deregulating c-myb expression in murine myeloid cells cotransformed by the cooperative activity of HoxA9 and Meis1. This effect is associated with an intronic positioning of epigenetic marks and RNA polymerase II binding in the orthologous region of a previously described alternative promoter for c-myb. Taken together, our results could provide a first hint to explain the abnormal expression of c-myb in leukemic cells

A Genome-Wide Study of Cytogenetic Changes in Colorectal Cancer Using SNP Microarrays: Opportunities for Future Personalized Treatment

In colorectal cancer (CRC), chromosomal instability (CIN) is typically studied using comparative-genomic hybridization (CGH) arrays. We studied paired (tumor and surrounding healthy) fresh frozen tissue from 86 CRC patients using Illumina's Infinium-based SNP array. This method allowed us to study CIN in CRC, with simultaneous analysis of copy number (CN) and B-allele frequency (BAF) - a representation of allelic composition. These data helped us to detect mono-allelic and bi-allelic amplifications/deletion, copy neutral loss of heterozygosity, and levels of mosaicism for mixed cell populations, some of which can not be assessed with other methods that do not measure BAF. We identified associations between CN abnormalities and different CRC phenotypes (histological diagnosis, location, tumor grade, stage, MSI and presence of lymph node metastasis). We showed commonalities between regions of CN change observed in CRC and the regions reported in previous studies of other solid cancers (e.g. amplifications of 20q, 13q, 8q, 5p and deletions of 18q, 17p and 8p). From Therapeutic Target Database, we identified relevant drugs, targeted to the genes located in these regions with CN changes, approved or in trials for other cancers and common diseases. These drugs may be considered for future therapeutic trials in CRC, based on personalized cytogenetic diagnosis. We also found many regions, harboring genes, which are not currently targeted by any relevant drugs that may be considered for future drug discovery studies. Our study shows the application of high density SNP arrays for cytogenetic study in CRC and its potential utility for personalized treatment

Dysregulation of Gene Expression in a Lysosomal Storage Disease Varies between Brain Regions Implicating Unexpected Mechanisms of Neuropathology

The characteristic neurological feature of many neurogenetic diseases is intellectual disability. Although specific neuropathological features have been described, the mechanisms by which specific gene defects lead to cognitive impairment remain obscure. To gain insight into abnormal functions occurring secondary to a single gene defect, whole transcriptome analysis was used to identify molecular and cellular pathways that are dysregulated in the brain in a mouse model of a lysosomal storage disorder (LSD) (mucopolysaccharidosis [MPS] VII). We assayed multiple anatomical regions separately, in a large cohort of normal and diseased mice, which greatly increased the number of significant changes that could be detected compared to past studies in LSD models. We found that patterns of aberrant gene expression and involvement of multiple molecular and cellular systems varied significantly between brain regions. A number of changes revealed unexpected system and process alterations, such as up-regulation of the immune system with few inflammatory changes (a significant difference from the closely related MPS IIIb model), down-regulation of major oligodendrocyte genes even though white matter changes are not a feature histopathologically, and a plethora of developmental gene changes. The involvement of multiple neural systems indicates that the mechanisms of neuropathology in this type of disease are much broader than previously appreciated. In addition, the variation in gene dysregulation between brain regions indicates that different neuropathologic mechanisms may predominate within different regions of a diseased brain caused by a single gene mutation

Coordinating Environmental Genomics and Geochemistry Reveals Metabolic Transitions in a Hot Spring Ecosystem

We have constructed a conceptual model of biogeochemical cycles and metabolic and microbial community shifts within a hot spring ecosystem via coordinated analysis of the “Bison Pool” (BP) Environmental Genome and a complementary contextual geochemical dataset of ∼75 geochemical parameters. 2,321 16S rRNA clones and 470 megabases of environmental sequence data were produced from biofilms at five sites along the outflow of BP, an alkaline hot spring in Sentinel Meadow (Lower Geyser Basin) of Yellowstone National Park. This channel acts as a >22 m gradient of decreasing temperature, increasing dissolved oxygen, and changing availability of biologically important chemical species, such as those containing nitrogen and sulfur. Microbial life at BP transitions from a 92°C chemotrophic streamer biofilm community in the BP source pool to a 56°C phototrophic mat community. We improved automated annotation of the BP environmental genomes using BLAST-based Markov clustering. We have also assigned environmental genome sequences to individual microbial community members by complementing traditional homology-based assignment with nucleotide word-usage algorithms, allowing more than 70% of all reads to be assigned to source organisms. This assignment yields high genome coverage in dominant community members, facilitating reconstruction of nearly complete metabolic profiles and in-depth analysis of the relation between geochemical and metabolic changes along the outflow. We show that changes in environmental conditions and energy availability are associated with dramatic shifts in microbial communities and metabolic function. We have also identified an organism constituting a novel phylum in a metabolic “transition” community, located physically between the chemotroph- and phototroph-dominated sites. The complementary analysis of biogeochemical and environmental genomic data from BP has allowed us to build ecosystem-based conceptual models for this hot spring, reconstructing whole metabolic networks in order to illuminate community roles in shaping and responding to geochemical variability

Unravelling the relationship between animal growth and immune response during micro-parasitic infections

Background: Both host genetic potentials for growth and disease resistance, as well as nutrition are known to affect responses of individuals challenged with micro-parasites, but their interactive effects are difficult to predict from experimental studies alone. Methodology/Principal Findings: Here, a mathematical model is proposed to explore the hypothesis that a host's response to pathogen challenge largely depends on the interaction between a host's genetic capacities for growth or disease resistance and the nutritional environment. As might be expected, the model predicts that if nutritional availability is high, hosts with higher growth capacities will also grow faster under micro-parasitic challenge, and more resistant animals will exhibit a more effective immune response. Growth capacity has little effect on immune response and resistance capacity has little effect on achieved growth. However, the influence of host genetics on phenotypic performance changes drastically if nutrient availability is scarce. In this case achieved growth and immune response depend simultaneously on both capacities for growth and disease resistance. A higher growth capacity (achieved e.g. through genetic selection) would be detrimental for the animal's ability to cope with pathogens and greater resistance may reduce growth in the short-term. Significance: Our model can thus explain contradicting outcomes of genetic selection observed in experimental studies and provides the necessary biological background for understanding the influence of selection and/or changes in the nutritional environment on phenotypic growth and immune response. © 2009 Doeschl-Wilson et al