Does the professional know their supracondylar from their gluteus maximus?

An international multiple centre investigation into nature versus nurture

Regulatory subunits of PKA define an axis of cellular proliferation/differentiation in ovarian cancer cells

<p>Abstract</p> <p>Background</p> <p>The regulatory subunit of cAMP-dependent protein kinase (PKA) exists in two isoforms, RI and RII, which distinguish the PKA isozymes, type I (PKA-I) and type II (PKA-II). Evidence obtained from a variety of different experimental approaches has shown that the relative levels of type I and type II PKA in cells can play a major role in determining the balance between cell growth and differentiation. In order to characterize the effect of PKA type I and type II regulatory subunits on gene transcription at a global level, the PKA regulatory subunit genes for RIα and RIIβ were stably transfected into cells of the ovarian cancer cell line (OVCAR8).</p> <p>Results</p> <p>RIα transfected cells exhibit hyper-proliferative growth and RIIβ transfected cells revert to a relatively quiescent state. Profiling by microarray revealed equally profound changes in gene expression between RIα, RIIβ, and parental OVCAR cells. Genes specifically up-regulated in RIα cells were highly enriched for pathways involved in cell growth while genes up-regulated in RIIβ cells were enriched for pathways involved in differentiation. A large group of genes (~3600) was regulated along an axis of proliferation/differentiation between RIα, parental, and RIIβ cells. RIα/wt and RIIβ/wt gene regulation was shown by two separate and distinct gene set analytical methods to be strongly cross-correlated with a generic model of cellular differentiation.</p> <p>Conclusion</p> <p>Overexpression of PKA regulatory subunits in an ovarian cancer cell line dramatically influences the cell phenotype. The proliferation phenotype is strongly correlated with recently identified clinical biomarkers predictive of poor prognosis in ovarian cancer suggesting a possible pivotal role for PKA regulation in disease progression.</p

Analysis of the TSC1and TSC2genes in sporadic renal cell carcinomas

The genetic events involved in the aetiology of non-clear-cell renal cell carcinoma (RCC) and a proportion of clear cell RCC remain to be defined. Germline mutations of the TSC1and TSC2genes cause tuberous sclerosis (TSC), a multi-system hamartoma syndrome that is also associated with RCC. We assessed 17 sporadic clear cell RCCs with a previously identified VHLmutation, 15 clear-cell RCCs without an identified VHLmutation and 15 non-clear-cell RCCs for loss of heterozygosity (LOH) at chromosomes 9q34 and 16p13.3, the chromosomal locations of TSC1and TSC2. LOH was detected in 4/9, 1/11 and 3/13 cases informative at both loci. SSCP analysis of the whole coding region of the retained allele did not reveal any cases with a detectable intragenic second somatic mutation. Furthermore, RT-PCR analysis of TSC1and TSC2on total RNA from 8 clear-cell RCC cell lines confirmed expression of both TSC genes. These data indicate that biallelic inactivation of TSC1or TSC2is not frequent in sporadic RCC and suggests that the molecular mechanisms of renal carcinogenesis in TSC are likely to be distinct. © 2001 Cancer Research Campaignhttp://www.bjcancer.com  http://www.bjcancer.co

TLR2 and TLR4 as Potential Biomarkers of Environmental Particulate Matter Exposed Human Myeloid Dendritic Cells

In many subjects who are genetically susceptible to asthma, exposure to environmental stimuli may exacerbate their condition. However, it is unknown how the expression and function of a family of pattern-recognition receptors called toll-like receptors (TLR) are affected by exposure to particulate pollution. TLRs serve a critical function in alerting the immune system of tissue damage or infection—the so-called “danger signals”. We are interested in the role that TLRs play in directing appropriate responses by innate immunity, particularly dendritic cells (DC), after exposing them to particulate pollution. Dendritic cells serve a pivotal role in directing host immunity. Thus, we hypothesized that alterations in TLR expression could be further explored as potential biomarkers of effect related to DC exposure to particulate pollution. We show some preliminary data that indicates that inhaled particulate pollution acts directly on DC by down-regulating TLR expression and altering the activation state of DC. While further studies are warranted, we suggest that alterations in TLR2 and TLR4 expression should be explored as potential biomarkers of DC exposure to environmental particulate pollution

Parametric hazard rate models for long-term sickness absence

PURPOSE: In research on the time to onset of sickness absence and the duration of sickness absence episodes, Cox proportional hazard models are in common use. However, parametric models are to be preferred when time in itself is considered as independent variable. This study compares parametric hazard rate models for the onset of long-term sickness absence and return to work. METHOD: Prospective cohort study on sickness absence with four follow-up years of 53,830 employees working in the private sector in the Netherlands. The time to onset of long-term (>6 weeks) sickness absence and return to work were modelled by parametric hazard rate models. RESULTS: The exponential parametric model with a constant hazard rate most accurately described the time to onset of long-term sickness absence. Gompertz-Makeham models with monotonically declining hazard rates best described return to work. CONCLUSIONS: Parametric models offer more possibilities than commonly used models for time-dependent processes as sickness absence and return to work. However, the advantages of parametric models above Cox models apply mainly for return to work and less for onset of long-term sickness absence

Exploration of the Mid-Cayman Rise

Oceanography articles are licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution, and reproduction in any medium or format as long as users cite the materials appropriately (e.g., authors, Oceanography, volume number, issue number, page number[s], figure number[s], and DOI for the article), provide a link to the Creative Commons license, and indicate the changes that were made to the original content

Time-Dependent c-Myc Transactomes Mapped by Array-Based Nuclear Run-On Reveal Transcriptional Modules in Human B Cells

The definition of transcriptional networks through measurements of changes in gene expression profiles and mapping of transcription factor binding sites is limited by the moderate overlap between binding and gene expression changes and the inability to directly measure global nuclear transcription (coined "transactome").We developed a method to measure nascent nuclear gene transcription with an Array-based Nuclear Run-On (ANRO) assay using commercial microarray platforms. This strategy provides the missing component, the transactome, to fully map transcriptional networks. ANRO measurements in an inducible c-Myc expressing human P493-6 B cell model reveals time-dependent waves of transcription, with a transactome early after c-Myc induction that does not persist at a late, steady-state phase, when genes that are regulated by c-Myc and E2F predominate. Gene set matrix analysis further uncovers functionally related groups of genes putatively regulated by waves of transcription factor motifs following Myc induction, starting with AP1 and CREB that are followed by EGR1, NFkB and STAT, and ending with E2F, Myc and ARNT/HIF motifs.By coupling ANRO with previous global mapping of c-Myc binding sites by chromatin immunoprecipitation (ChIP) in P493-6 cells, we define a set of transcriptionally regulated direct c-Myc target genes and pave the way for the use of ANRO to comprehensively map any transcriptional network

Breaking the Matches in a Paired T-Test for Community Interventions When the Number of Pairs is Small

There is considerable interest in community interventions for health promotion, where the community is the experimental unit. Because such interventions are expensive, the number of experimental units (communities) is usually small. Because of the small number of communities involved, investigators often match treatment and control communities on demographic variables before randomization to minimize the possibility of a bad split. Unfortunately, matching has been shown to decrease the power of the design when the number of pairs is small, unless the matching variable is very highly correlated with the outcome variable (in this case, with change in the health behavior). We used computer simulation to examine the performance of an approach in which we matched communities but performed an unmatched analysis. If the appropriate matching variables are unknown, and there are fewer than ten pairs, an unmatched design and analysis has the most power. If, however, one prefers a matched design, then for N \u3c 10, power can be increased by performing an unmatched analysis of the matched data. We also discuss a variant of this procedure, in which an unmatched analysis is performed only if the matching didn\u27t work