Multiplexing schemes for generic SNP genotyping assays.

Association studies in populations relate genomic variation among individuals with medical condition. Key to these studies is the development of efficient and affordable genotyping techniques. Generic genotyping assays are independent of the target SNPs and offer great flexibility in the genotyping process. Efficient use of such assays calls for identifying sets of SNPs that can be interrogated in parallel under constraints imposed by the genotyping technology. In this paper, we study problems arising in the design of genotyping experiments using generic assays. Our problem formulation deals with two main factors that affect the genotyping cost: the number of assays used and the number of PCR reactions required for sample preparation. We prove that the resulting computational problems are hard, but provide approximate and heuristic solutions to these problems. Our algorithmic approach is based on recasting the multiplexing problems as partitioning and packing problems on a bipartite graph. We tested our algorithmic approaches on an extensive collection of synthetic data and on data that was simulated using real SNP sequences. Our results show that the algorithms achieve near-optimal designs in many cases and demonstrate the applicability of generic assays to SNP genotyping.     

Tissue classification with gene expression profiles.

Constantly improving gene expression profiling technologies are expected to provide understanding and insight into cancer-related cellular processes. Gene expression data is also expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. In this work we examine three sets of gene expression data measured across sets of tumor(s) and normal clinical samples: The first set consists of 2,000 genes, measured in 62 epithelial colon samples (Alon et al., 1999). The second consists of approximately equal to 100,000 clones, measured in 32 ovarian samples (unpublished extension of data set described in Schummer et al. (1999)). The third set consists of approximately equal to 7,100 genes, measured in 72 bone marrow and peripheral blood samples (Golub et al, 1999). We examine the use of scoring methods, measuring separation of tissue type (e.g., tumors from normals) using individual gene expression levels. These are then coupled with high-dimensional classification methods to assess the classification power of complete expression profiles. We present results of performing leave-one-out cross validation (LOOCV) experiments on the three data sets, employing nearest neighbor classifier, SVM (Cortes and Vapnik, 1995), AdaBoost (Freund and Schapire, 1997) and a novel clustering-based classification technique. As tumor samples can differ from normal samples in their cell-type composition, we also perform LOOCV experiments using appropriately modified sets of genes, attempting to eliminate the resulting bias. We demonstrate success rate of at least 90% in tumor versus normal classification, using sets of selected genes, with, as well as without, cellular-contamination-related members. These results are insensitive to the exact selection mechanism, over a certain range.     

Behavioral patterns of bats at a wind turbine confirm seasonality of fatality risk

Bat fatalities at wind energy facilities in North America are predominantly comprised of migratory, tree‐dependent species, but it is unclear why these bats are at higher risk. Factors influencing bat susceptibility to wind turbines might be revealed by temporal patterns in their behaviors around these dynamic landscape structures. In northern temperate zones, fatalities occur mostly from July through October, but whether this reflects seasonally variable behaviors, passage of migrants, or some combination of factors remains unknown. In this study, we examined video imagery spanning one year in the state of Colorado in the United States, to characterize patterns of seasonal and nightly variability in bat behavior at a wind turbine. We detected bats on 177 of 306 nights representing approximately 3,800 hr of video and > 2,000 discrete bat events. We observed bats approaching the turbine throughout the night across all months during which bats were observed. Two distinct seasonal peaks of bat activity occurred in July and September, representing 30% and 42% increases in discrete bat events from the preceding months June and August, respectively. Bats exhibited behaviors around the turbine that increased in both diversity and duration in July and September. The peaks in bat events were reflected in chasing and turbine approach behaviors. Many of the bat events involved multiple approaches to the turbine, including when bats were displaced through the air by moving blades. The seasonal and nightly patterns we observed were consistent with the possibility that wind turbines invoke investigative behaviors in bats in late summer and autumn coincident with migration and that bats may return and fly close to wind turbines even after experiencing potentially disruptive stimuli like moving blades. Our results point to the need for a deeper understanding of the seasonality, drivers, and characteristics of bat movement across spatial scales.     

Identification of candidate genes encoding an LDL-C QTL in baboons

Cardiovascular disease (CVD) is the leading cause of death in developed countries, and dyslipidemia is a major risk factor for CVD. We previously identified a cluster of quantitative trait loci (QTL) on baboon chromosome 11 for multiple, related quantitative traits for serum LDL-cholesterol (LDL-C). Here we report differentially regulated hepatic genes encoding an LDL-C QTL that influences LDL-C levels in baboons. We performed hepatic whole-genome expression profiling for LDL-C-discordant baboons fed a high-cholesterol, high-fat (HCHF) diet for seven weeks. We detected expression of 117 genes within the QTL 2-LOD support interval. Three genes were differentially expressed in low LDL-C responders and 8 in high LDL-C responders in response to a HCHF diet. Seven genes (ACVR1B, CALCOCO1, DGKA, ERBB3, KRT73, MYL6B, TENC1) showed discordant expression between low and high LDL-C responders. To prioritize candidate genes, we integrated miRNA and mRNA expression profiles using network tools and found that four candidates (ACVR1B, DGKA, ERBB3, TENC1) were miRNA targets and that the miRNAs were inversely expressed to the target genes. Candidate gene expression was validated using QRT-PCR and Western blotting. This study reveals candidate genes that influence variation in LDL-C in baboons and potential genetic mechanisms for further investigation.     

Efficient calculation of interval scores for DNA copy number data analysis.

DNA amplifications and deletions characterize cancer genome and are often related to disease evolution. Microarray-based techniques for measuring these DNA copy-number changes use fluorescence ratios at arrayed DNA elements (BACs, cDNA, or oligonucleotides) to provide signals at high resolution, in terms of genomic locations. These data are then further analyzed to map aberrations and boundaries and identify biologically significant structures. We develop a statistical framework that enables the casting of several DNA copy number data analysis questions as optimization problems over real-valued vectors of signals. The simplest form of the optimization problem seeks to maximize phi(I) = Sigmanu(i)/radical|I| over all subintervals I in the input vector. We present and prove a linear time approximation scheme for this problem, namely, a process with time complexity O (nepsilon(-2)) that outputs an interval for which phi(I) is at least Opt/alpha(epsilon), where Opt is the actual optimum and alpha(epsilon) --> 1 as epsilon --> 0. We further develop practical implementations that improve the performance of the naive quadratic approach by orders of magnitude. We discuss properties of optimal intervals and how they apply to the algorithm performance. We benchmark our algorithms on synthetic as well as publicly available DNA copy number data. We demonstrate the use of these methods for identifying aberrations in single samples as well as common alterations in fixed sets and subsets of breast cancer samples.     

Are primed polymorphonuclear leukocytes contributors to the high heparanase levels in hemodialysis patients?

Patients on chronic hemodialysis (HD) are at high risk for developing atherosclerosis and cardiovascular complications. Heparanase, an endoglycosidase that cleaves heparan sulfate (HS) side chains of proteoglycans, is involved in extracellular matrix degradation and, as such, may be involved in the atherosclerotic lesion progression. We hypothesize that heparanase is elevated in HD patients, partly due to its release from primed circulating polymorphonuclear leukocytes (PMNLs), undergoing degranulation. Priming of PMNLs was assessed by levels of CD11b and the rate of superoxide release. Heparanase mRNA expression in PMNLs was determined by RT-PCR. PMNL and plasma levels of heparanase were determined by immunoblotting, immunofluorescence, and flow cytometry analyses. The levels of soluble HS in plasma were measured by a competition ELISA. This study shows that PMNLs isolated from HD patients have higher mRNA and protein levels of heparanase compared with normal control (NC) subjects and that heparanase levels correlate positively with PMNL priming. Plasma levels of heparanase were higher in HD patients than in NC subjects and were further elevated after the dialysis session. In addition, heparanase expression inversely correlates with plasma HS levels. A pronounced expression of heparanase was found in human atherosclerotic lesions. The increased heparanase activity in the blood of HD patients results at least in part from the degranulation of primed PMNLs and may contribute to the acceleration of the atherosclerotic process. Our findings highlight primed PMNLs as a possible source for the increased heparanase in HD patients, posing heparanase as a new risk factor for cardiovascular complications and atherosclerosis.     

Effects of estrone and 17 β-estradiol on vegetative growth of Medicago sativa

Alfalfa ( Medicago sativa L.) plants were grown in the absence or presence of the steroidal estrogens, estrone and 17β-estradiol, under varying conditions. Plants were analysed for the following parameters: plant weight, estrogen content, phytoestrogen content, degree of nodulation and nitrogen fixation activity. It was found that under controlled greenhouse conditions: (1) Treatment with estrogens in the range of 0.005 to 0.5 μg 1⁻¹ increases both shoot and root dry weitht. In contrast, estrogen in concentrations of 50 to 500 μg 1⁻¹ decreases plant growth. (2) The effect of estrogen of growth is most marked in the absence of nitrogen. (3) Estrone is more effective in increasing growth than 17 β-estradiol. (4) In the plants where estrogen induced growth there was no significant increase in nitrogen fixation activity and nodule number. (5) Endogenous estrogen content of the plant did not increase at concentrations (0.005-0.5 μg 1⁻¹) which increased vegetative growth. (6) Endogenous estrogen content of the plant did increase at concentrations of estrogen (50-500 μg 1⁻¹ which inhibited vegetative growth and nodule weight. It can be concluded that estrogen in concentrations found in sewage water (0.3 μg estrogen 1⁻¹) can affect the vegetative growth of alfalfa plants.     

Age-adjusted antitumoral therapy based on the demonstration of increased apoptosis as a mechanism underlying the reduced malignancy of tumors in the aged

In view of the constant increase in the aged population, age-adjusted cancer therapy becomes an urgent target. Although cancer incidence rises with age, paradoxically, growth rate and metastasis often proceed at a slower rate in the aged. Determining the mechanism(s) underlying this reduced tumor progression in the old might have implications for a rational design of age-adjusted therapy. Thus far, decreased cell proliferation or immune response modifications were suggested as possible mechanisms. We show here that an increased tendency to apoptotic tumor cell death in the aged could constitute an additional mechanism. Based on this mechanism, we compared the therapeutic efficacy of two apoptosis inducers, hydrocortisone and adriamycin, on AKR lymphoma and B16 melanoma growth in young and old mice. Treatment with hydrocortisone acetate inhibited tumor growth practically only in old mice in the two tumor systems. Similar effects were obtained with adriamycin treatment of AKR lymphoma but opposite results were seen with B16 melanoma. We thus demonstrated, in three of the four tumor-therapeutic modality systems examined, an age-related antitumoral efficacy of two apoptosis-inducing agents, with tendency for a remarkably more pronounced effect in aged mice.