Meta-Analysis of Placental Transcriptome Data Identifies a Novel Molecular Pathway Related to Preeclampsia.

Studies using the placental transcriptome to identify key molecules relevant for preeclampsia are hampered by a relatively small sample size. In addition, they use a variety of bioinformatics and statistical methods, making comparison of findings challenging. To generate a more robust preeclampsia gene expression signature, we performed a meta-analysis on the original data of 11 placenta RNA microarray experiments, representing 139 normotensive and 116 preeclamptic pregnancies. Microarray data were pre-processed and analyzed using standardized bioinformatics and statistical procedures and the effect sizes were combined using an inverse-variance random-effects model. Interactions between genes in the resulting gene expression signature were identified by pathway analysis (Ingenuity Pathway Analysis, Gene Set Enrichment Analysis, Graphite) and protein-protein associations (STRING). This approach has resulted in a comprehensive list of differentially expressed genes that led to a 388-gene meta-signature of preeclamptic placenta. Pathway analysis highlights the involvement of the previously identified hypoxia/HIF1A pathway in the establishment of the preeclamptic gene expression profile, while analysis of protein interaction networks indicates CREBBP/EP300 as a novel element central to the preeclamptic placental transcriptome. In addition, there is an apparent high incidence of preeclampsia in women carrying a child with a mutation in CREBBP/EP300 (Rubinstein-Taybi Syndrome). The 388-gene preeclampsia meta-signature offers a vital starting point for further studies into the relevance of these genes (in particular CREBBP/EP300) and their concomitant pathways as biomarkers or functional molecules in preeclampsia. This will result in a better understanding of the molecular basis of this disease and opens up the opportunity to develop rational therapies targeting the placental dysfunction causal to preeclampsia.

Validation of the Charlson Comorbidity Index in acutely hospitalized elderly adults: a prospective cohort study.

OBJECTIVES: To determine whether the Charlson Comorbidity Index (CCI) predicts short- and long-term mortality. DESIGN: Prospective cohort study. SETTING: The medical department of two university hospitals and one community-based hospital. PARTICIPANTS: Acutely hospitalized individuals aged 65 and older with a mean age of 77.8 ± 7.9, 45.8% male (n = 1,313). MEASUREMENTS: In eligible persons, information on demographic characteristics, activities of daily living (modified Katz ADL Index score), and disease-related measures was collected within 48 hours after admission. Follow-up using self-reporting questionnaires was performed at 3 months and 1 year. Functional decline was defined as a decline of at least 1 point on the modified Katz ADL Index score at 12 months from baseline. Mortality data at 3 months and 1 and 5 years were collected from the municipal database. RESULTS: Logistic regression analysis, adjusted for age and sex, showed that participants with a CCI of 5 or more had higher 3-month (odds ratio (OR) = 3.6, 95% confidence interval (CI) = 2.1-6.4), 1-year (OR = 7.1, 95% CI = 4.2-11.9), and 5-year (OR = 52.4, 95% CI = 13.3-206.4) mortality than those with a CCI of 0. Participants with CCI scores between 1 and 4 also had greater mortality risk at 3 months and 1 and 5 years. CONCLUSION: The CCI independently predicts short- and long-term mortality in acutely ill hospitalized elderly adults.

Differentially expressed genes in the pre-eclamptic placenta: a systematic review and meta-analysis.

OBJECTIVE: To systematically review the literature on human gene expression data of placental tissue in pre-eclampsia and to characterize a meta-signature of differentially expressed genes in order to identify novel putative diagnostic markers. DATA SOURCES: Medline through 11 February 2011 using MeSH terms and keywords related to placenta, gene expression and gene expression arrays; GEO database using the term "placent*"; and reference lists of eligible primary studies, without constraints. METHODS: From 1068 studies retrieved from the search, we included original publications that had performed gene expression array analyses of placental tissue in the third trimester and that reported on differentially expressed genes in pre-eclampsia versus normotensive controls. Two reviewers independently identified eligible studies, extracted descriptive and gene expression data and assessed study quality. Using a vote-counting method based on a comparative meta-profiling algorithm, we determined a meta-signature that characterizes the significant intersection of differentially expressed genes from the collection of independent gene signatures. RESULTS: We identified 33 eligible gene expression array studies of placental tissue in the 3(rd) trimester comprising 30 datasets on mRNA expression and 4 datasets on microRNA expression. The pre-eclamptic placental meta-signature consisted of 40 annotated gene transcripts and 17 microRNAs. At least half of the mRNA transcripts encode a protein that is secreted from the cell and could potentially serve as a biomarker. CONCLUSIONS: In addition to well-known and validated genes, we identified 14 transcripts not reported previously in relation to pre-eclampsia of which the majority is also expressed in the 1(st) trimester placenta, and three encode a secreted protein.

Rest-activity patterns in patients with delirium.

OBJECTIVE: Delirium is a frequent syndrome in elderly hospital patients. Symptoms typically show a fluctuating course during the day, with patients exhibiting disturbances of their sleep-wake rhythm. Delirium is frequently underdiagnosed, especially the so-called hypoactive subtype. Devices measuring 24-hr motor patterns could contribute to the recognition of delirium. The purpose of this paper is two-fold. First, the results of a pilot study are presented, in which 24-hr motor patterns of delirious patients are measured with a wrist-actigraph. Second, studies reporting 24-hr motor patterns in delirious patients are systematically reviewed. METHODS: The pilot study included 9 patients, 65 years or older, with a hip fracture in need of surgical repair. For the review, MEDLINE and Embase were searched for studies on motor activity assessment in delirious patients. RESULTS: In the pilot study, the 24-hr activity rhythm was severely disturbed during delirium, and most actigraphic sleep parameter estimates indicated significantly worse sleep during delirious nights. The systematic search resulted in 10 papers. In 3 papers, the sleep-wake rhythm of delirious patients was significantly different from that of nondelirious patients. In 5 papers, delirious patients could be classified into delirium subtypes. In the 2 remaining papers, 24-hr motor patterns of delirium subtypes were not significantly different. CONCLUSION: Activity patterns revealed differences between delirious and nondelirious patients and between the different subtypes, even in small samples of patients. Future studies, with preferably larger sample sizes, should confirm the potential of activity pattern measuring devices in the early detection of delirium.

Computational identification of insertional mutagenesis targets for cancer gene discovery.

Insertional mutagenesis is a potent forward genetic screening technique used to identify candidate cancer genes in mouse model systems. An important, yet unresolved issue in the analysis of these screens, is the identification of the genes affected by the insertions. To address this, we developed Kernel Convolved Rule Based Mapping (KC-RBM). KC-RBM exploits distance, orientation and insertion density across tumors to automatically map integration sites to target genes. We perform the first genome-wide evaluation of the association of insertion occurrences with aberrant gene expression of the predicted targets in both retroviral and transposon data sets. We demonstrate the efficiency of KC-RBM by showing its superior performance over existing approaches in recovering true positives from a list of independently, manually curated cancer genes. The results of this work will significantly enhance the accuracy and speed of cancer gene discovery in forward genetic screens. KC-RBM is available as R-package.

Insertional mutagenesis in mice deficient for p15Ink4b, p16Ink4a, p21Cip1, and p27Kip1 reveals cancer gene interactions and correlations with tumor phenotypes.

The cyclin dependent kinase (CDK) inhibitors p15, p16, p21, and p27 are frequently deleted, silenced, or downregulated in many malignancies. Inactivation of CDK inhibitors predisposes mice to tumor development, showing that these genes function as tumor suppressors. Here, we describe high-throughput murine leukemia virus insertional mutagenesis screens in mice that are deficient for one or two CDK inhibitors. We retrieved 9,117 retroviral insertions from 476 lymphomas to define hundreds of loci that are mutated more frequently than expected by chance. Many of these loci are skewed toward a specific genetic context of predisposing germline and somatic mutations. We also found associations between these loci with gender, age of tumor onset, and lymphocyte lineage (B or T cell). Comparison of retroviral insertion sites with single nucleotide polymorphisms associated with chronic lymphocytic leukemia revealed a significant overlap between the datasets. Together, our findings highlight the importance of genetic context within large-scale mutation detection studies, and they show a novel use for insertional mutagenesis data in prioritizing disease-associated genes that emerge from genome-wide association studies.

Biclustering sparse binary genomic data.

Genomic datasets often consist of large, binary, sparse data matrices. In such a dataset, one is often interested in finding contiguous blocks that (mostly) contain ones. This is a biclustering problem, and while many algorithms have been proposed to deal with gene expression data, only two algorithms have been proposed that specifically deal with binary matrices. None of the gene expression biclustering algorithms can handle the large number of zeros in sparse binary matrices. The two proposed binary algorithms failed to produce meaningful results. In this article, we present a new algorithm that is able to extract biclusters from sparse, binary datasets. A powerful feature is that biclusters with different numbers of rows and columns can be detected, varying from many rows to few columns and few rows to many columns. It allows the user to guide the search towards biclusters of specific dimensions. When applying our algorithm to an input matrix derived from TRANSFAC, we find transcription factors with distinctly dissimilar binding motifs, but a clear set of common targets that are significantly enriched for GO categories.

Large-scale mutagenesis in p19(ARF)- and p53-deficient mice identifies cancer genes and their collaborative networks.

p53 and p19(ARF) are tumor suppressors frequently mutated in human tumors. In a high-throughput screen in mice for mutations collaborating with either p53 or p19(ARF) deficiency, we identified 10,806 retroviral insertion sites, implicating over 300 loci in tumorigenesis. This dataset reveals 20 genes that are specifically mutated in either p19(ARF)-deficient, p53-deficient or wild-type mice (including Flt3, mmu-mir-106a-363, Smg6, and Ccnd3), as well as networks of significant collaborative and mutually exclusive interactions between cancer genes. Furthermore, we found candidate tumor suppressor genes, as well as distinct clusters of insertions within genes like Flt3 and Notch1 that induce mutants with different spectra of genetic interactions. Cross species comparative analysis with aCGH data of human cancer cell lines revealed known and candidate oncogenes (Mmp13, Slamf6, and Rreb1) and tumor suppressors (Wwox and Arfrp2). This dataset should prove to be a rich resource for the study of genetic interactions that underlie tumorigenesis.