Bayesian DNA copy number analysis

BACKGROUND: Some diseases, like tumors, can be related to chromosomal aberrations, leading to changes of DNA copy number. The copy number of an aberrant genome can be represented as a piecewise constant function, since it can exhibit regions of deletions or gains. Instead, in a healthy cell the copy number is two because we inherit one copy of each chromosome from each our parents. Bayesian Piecewise Constant Regression (BPCR) is a Bayesian regression method for data that are noisy observations of a piecewise constant function. The method estimates the unknown segment number, the endpoints of the segments and the value of the segment levels of the underlying piecewise constant function. The Bayesian Regression Curve (BRC) estimates the same data with a smoothing curve. However, in the original formulation, some estimators failed to properly determine the corresponding parameters. For example, the boundary estimator did not take into account the dependency among the boundaries and succeeded in estimating more than one breakpoint at the same position, losing segments. RESULTS: We derived an improved version of the BPCR (called mBPCR) and BRC, changing the segment number estimator and the boundary estimator to enhance the fitting procedure. We also proposed an alternative estimator of the variance of the segment levels, which is useful in case of data with high noise. Using artificial data, we compared the original and the modified version of BPCR and BRC with other regression methods, showing that our improved version of BPCR generally outperformed all the others. Similar results were also observed on real data. CONCLUSION: We propose an improved method for DNA copy number estimation, mBPCR, which performed very well compared to previously published algorithms. In particular, mBPCR was more powerful in the detection of the true position of the breakpoints and of small aberrations in very noisy data. Hence, from a biological point of view, our method can be very useful, for example, to find targets of genomic aberrations in clinical cancer samples

An integrated Bayesian analysis of LOH and copy number data

BACKGROUND Cancer and other disorders are due to genomic lesions. SNP-microarrays are able to measure simultaneously both genotype and copy number (CN) at several Single Nucleotide Polymorphisms (SNPs) along the genome. CN is defined as the number of DNA copies, and the normal is two, since we have two copies of each chromosome. The genotype of a SNP is the status given by the nucleotides (alleles) which are present on the two copies of DNA. It is defined homozygous or heterozygous if the two alleles are the same or if they differ, respectively. Loss of heterozygosity (LOH) is the loss of the heterozygous status due to genomic events. Combining CN and LOH data, it is possible to better identify different types of genomic aberrations. For example, a long sequence of homozygous SNPs might be caused by either the physical loss of one copy or a uniparental disomy event (UPD), i.e. each SNP has two identical nucleotides both derived from only one parent. In this situation, the knowledge of the CN can help in distinguishing between these two events. RESULTS To better identify genomic aberrations, we propose a method (called gBPCR) which infers the type of aberration occurred, taking into account all the possible influence in the microarray detection of the homozygosity status of the SNPs, resulting from an altered CN level. Namely, we model the distributions of the detected genotype, given a specific genomic alteration and we estimate the parameters involved on public reference datasets. The estimation is performed similarly to the modified Bayesian Piecewise Constant Regression, but with improved estimators for the detection of the breakpoints.Using artificial and real data, we evaluate the quality of the estimation of gBPCR and we also show that it outperforms other well-known methods for LOH estimation. CONCLUSIONS We propose a method (gBPCR) for the estimation of both LOH and CN aberrations, improving their estimation by integrating both types of data and accounting for their relationships. Moreover, gBPCR performed very well in comparison with other methods for LOH estimation and the estimated CN lesions on real data have been validated with another technique.This work was supported by Swiss National Science Foundation (grants 205321-112430, 205320-121886/1); Oncosuisse grants OCS-1939-8-2006 and OCS - 02296-08-2008; Cantone Ticino ("Computational life science/Ticino in rete” program); Fondazione per la Ricerca e la Cura sui Linfomi (Lugano, Switzerland)

Role of Phytoestrogen Ferutinin in Preventing/Recovering Bone Loss: Results from Experimental Ovariectomized Rat Models

In the Chapter 35 of the book are reported observations of recent pubblications on the effect of ferutinin in preventing/recovering severe osteoporosis secondary to ovariectomy in rats. On the basis of the results so far obtained, the authors suggest to enumerate ferutinin among the osteoprotective substances. This fact acquires a more relevant importance in the light of recent tenable evidences reported from various authors concerning the absence of negative side effects by some phytoestrogens (particularly genistein, 8-prenylnaringenin, reveratrol and red clover extract) on the tropism of various organs commonly targeted by estrogens. In conclusion, the results reported not only provide evidence that ferutinin can significantly prevent/recover ovariectomy-induced bone loss in rats, but also that it could protect against the onset of uterus cancer. Although the putative undesired estrogenic-like side effects on uterus of such phytoestrogen have not yet been fully investigated, ferutinin could be an interesting safer alternative new candidate for HRT in treatment of post-menopausal symptoms, since it seems to protect from bone loss induced by ovariectomy (Palumbo et al., 2009; Ferretti et al., 2010) and in part to mime the ovarian endocrine function during menopause

Poverty work program: poverty reduction in Nigeria in the last decade

The report consists of four chapters. Chapter one profiles the trends in growth, household consumption, and poverty rates at the national level between 2004 and 2013. Descriptive statistics of consumption and selected poverty indexes are presented and a profile of the characteristics of the poor is given. The chapter concludes with an analysis of nonmonetary indicators. Chapter two unpackages the national level data into subnational results (six zones) and shows the high and increasing divide of socioeconomic indicators. Chapter three uses descriptive and econometric technics to identify the drivers of this divide. Chapter four concludes and provides a road map for policy action to effectively address this divide

Phenotypical Characterization and Clinical Outcome of Canine Burkitt-Like Lymphoma

In dogs, Burkitt-like lymphoma (B-LL) is rare tumor and it is classified as a high-grade B-cell malignancy. The diagnosis is challenging because of the similar histologic appearance with other histotypes, no defined phenotypical criteria and poorly described clinical aspects. The aim of the study was to provide a detailed description of clinical and morphological features, as well as immunophenotypical profile of B-LL in comparison with the human counterpart. Thirteen dogs with histologically proven B-LL, for which a complete staging and follow-up were available, were retrospectively selected. Immunohistochemical expression of CD20, PAX5, CD3, CD10, BCL2, BCL6, MYC, and caspase-3 was evaluated. Histologically, all B-LLs showed a diffuse architecture with medium to large-sized cells, high mitotic rate and diffuse starry sky appearance. B-phenotype of neoplastic cells was confirmed both by flow-cytometry and immunohistochemistry. Conversely, B-LLs were negative for BCL2 and MYC, whereas some cases co-expressed BCL6 and CD10, suggesting a germinal center B-cell origin. Disease stage was advanced in the majority of cases. All dogs received CHOP-based chemotherapy with or without immunotherapy. Despite treatment, prognosis was poor, with a median time to progression and survival of 130 and 228 days, respectively. Nevertheless, ~30% of dogs survived more than 1 year. An increased apoptotic index, a high turnover index and caspase-3 index correlated with shorter survival. In conclusion, canine B-LL shows phenotypical differences with the human counterpart along with features that might help to differentiate this entity from diffuse large B-cell lymphoma

A cyanobacterial LPS antagonist prevents endotoxin shock and blocks sustained TLR4 stimulation required for cytokine expression

Toll-like receptors (TLRs) function as primary sensors that elicit coordinated innate immune defenses through recognition of microbial products and induction of immune and proinflammatory genes. Here we report the identification and biological characterization of a lipopolysaccharide (LPS)-like molecule extracted from the cyanobacterium Oscillatoria Planktothrix FP1 (cyanobacterial product [CyP]) that is not stimulatory per se but acts as a potent and selective antagonist of bacterial LPS. CyP binds to MD-2 and efficiently competes with LPS for binding to the TLR4–MD-2 receptor complex. The addition of CyP together with LPS completely inhibited both MyD88- and TRIF-dependent pathways and suppressed the whole LPS-induced gene transcription program in human dendritic cells (DCs). CyP protected mice from endotoxin shock in spite of a lower capacity to inhibit LPS stimulation of mouse DCs. Interestingly, the delayed addition of CyP to DCs responding to LPS strongly inhibited signaling and cytokine production by immediate down-regulation of inflammatory cytokine mRNAs while not affecting other aspects of DC maturation, such as expression of major histocompatibility complex molecules, costimulatory molecules, and CCR7. Collectively, these results indicate that CyP is a potent competitive inhibitor of LPS in vitro and in vivo and reveal the requirement of sustained TLR4 stimulation for induction of cytokine genes in human DCs

T-Cell Leukemia/Lymphoma 1 (TCL1): An Oncogene Regulating Multiple Signaling Pathways

Almost 30 years ago, Carlo Croce's group discovered the T-Cell Leukemia/Lymphoma 1A oncogene (TCL1A or TCL1). TCL1 protein is normally expressed in fetal tissues and early developmental stage lymphocytes. Its expression is deregulated in chronic lymphocytic leukemia (B-CLL) and most lymphomas. TCL1 plays a central role in lymphomagenesis as a co-activator of AKT kinases and other recently elucidated interacting protein partners. These include ATM, HSP70 and TP63, which were all confirmed as binding partners of TCL1 from co-immunoprecipitation experiments utilizing endogenously expressed proteins. The nature of these interactions highlighted the role of TCL1 in enhancing multiple signaling pathways, including PI3K and NF-κB. Based on its role in the aforementioned pathways and, despite the lack of a well-defined enzymatic activity, TCL1 is considered a potential therapeutic target for TCL1-positive hematological malignancies. This perspective will provide an overview of TCL1A and its interacting partners

PCSF: An R-package for network-based interpretation of high-throughput data

With the recent technological developments a vast amount of high-throughput data has been profiled to understand the mechanism of complex diseases. The current bioinformatics challenge is to interpret the data and underlying biology, where efficient algorithms for analyzing heterogeneous high-throughput data using biological networks are becoming increasingly valuable. In this paper, we propose a software package based on the Prize-collecting Steiner Forest graph optimization approach. The PCSF package performs fast and user-friendly network analysis of high-throughput data by mapping the data onto a biological networks such as protein-protein interaction, gene-gene interaction or any other correlation or coexpression based networks. Using the interaction networks as a template, it determines high-confidence subnetworks relevant to the data, which potentially leads to predictions of functional units. It also interactively visualizes the resulting subnetwork with functional enrichment analysis