Delineating Genetic Alterations for Tumor Progression in the MCF10A Series of Breast Cancer Cell Lines

To gain insight into the role of genomic alterations in breast cancer progression, we conducted a comprehensive genetic characterization of a series of four cell lines derived from MCF10A. MCF10A is an immortalized mammary epithelial cell line (MEC); MCF10AT is a premalignant cell line generated from MCF10A by transformation with an activated HRAS gene; MCF10CA1h and MCF10CA1a, both derived from MCF10AT xenografts, form well-differentiated and poorly-differentiated malignant tumors in the xenograft models, respectively. We analyzed DNA copy number variation using the Affymetrix 500 K SNP arrays with the goal of identifying gene-specific amplification and deletion events. In addition to a previously noted deletion in the CDKN2A locus, our studies identified MYC amplification in all four cell lines. Additionally, we found intragenic deletions in several genes, including LRP1B in MCF10CA1h and MCF10CA1a, FHIT and CDH13 in MCF10CA1h, and RUNX1 in MCF10CA1a. We confirmed the deletion of RUNX1 in MCF10CA1a by DNA and RNA analyses, as well as the absence of the RUNX1 protein in that cell line. Furthermore, we found that RUNX1 expression was reduced in high-grade primary breast tumors compared to low/mid-grade tumors. Mutational analysis identified an activating PIK3CA mutation, H1047R, in MCF10CA1h and MCF10CA1a, which correlates with an increase of AKT1 phosphorylation at Ser473 and Thr308. Furthermore, we showed increased expression levels for genes located in the genomic regions with copy number gain. Thus, our genetic analyses have uncovered sequential molecular events that delineate breast tumor progression. These events include CDKN2A deletion and MYC amplification in immortalization, HRAS activation in transformation, PIK3CA activation in the formation of malignant tumors, and RUNX1 deletion associated with poorly-differentiated malignant tumors

Genetic and Molecular Functional Characterization of Variants within TNFSF13B, a Positional Candidate Preeclampsia Susceptibility Gene on 13q

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Mechanism and specificity of pentachloropseudilin-mediated inhibition of myosin motor activity.

Here, we report that the natural compound pentachloropseudilin (PClP) acts as a reversible and allosteric inhibitor of myosin ATPase and motor activity. IC(50) values are in the range from 1 to 5 μm for mammalian class-1 myosins and greater than 90 μm for class-2 and class-5 myosins, and no inhibition was observed with class-6 and class-7 myosins. We show that in mammalian cells, PClP selectively inhibits myosin-1c function. To elucidate the structural basis for PClP-induced allosteric coupling and isoform-specific differences in the inhibitory potency of the compound, we used a multifaceted approach combining direct functional, crystallographic, and in silico modeling studies. Our results indicate that allosteric inhibition by PClP is mediated by the combined effects of global changes in protein dynamics and direct communication between the catalytic and allosteric sites via a cascade of small conformational changes along a conserved communication pathway

Polymorphisms in the gene regions of the adaptor complex LAMTOR2/LAMTOR3 and their association with breast cancer risk.

Background: The late endosomal LAMTOR complex serves as a convergence point for both the RAF/MEK/ERK and the PI3K/AKT/mTOR pathways. Interestingly, both of these signalling cascades play a significant role in the aetiology of breast cancer. Our aim was to address the possible role of genetic polymorphisms in LAMTOR2 and LAMTOR3 as genetic risk factors for breast cancer. Methodology/Results: We sequenced the exons and exon-intron boundaries of LAMTOR2 (p14) and LAMTOR3 (MP1) in 50 prospectively collected pairs of cancerous tissue and blood samples from breast cancer patients and compared their genetic variability. We found one single nucleotide polymorphism (SNP) in LAMTOR2 (rs7541) and two SNPs in LAMTOR3 (rs2298735 and rs148972953) in both tumour and blood samples, but no somatic mutations in cancerous tissues. In addition, we genotyped all three SNPs in 296 samples from the Risk Prediction of Breast Cancer Metastasis Study and found evidence of a genetic association between rs148972953 and oestrogen (ER) and progesterone receptor negative status (PR) (ER: OR = 3.60 (1.15-11.28); PR: OR = 4.27 (1.43-12.72)). However, when we additionally genotyped rs148972953 in the MARIE study including 2,715 breast cancer cases and 5,216 controls, we observed neither a difference in genotype frequencies between patients and controls nor was the SNP associated with ER or PR. Finally, all three SNPs were equally frequent in breast cancer samples and female participants (n = 640) of the population-based SAPHIR Study. Conclusions: The identified polymorphisms in LAMTOR2 and LAMTOR3 do not seem to play a relevant role in breast cancer. Our work does not exclude a role of other not yet identified SNPs or that the here annotated polymorphism may in fact play a relevant role in other diseases. Our results underscore the importance of replication in association studies

Friends and Foes from an Ant Brain's Point of View – Neuronal Correlates of Colony Odors in a Social Insect

Background: Successful cooperation depends on reliable identification of friends and foes. Social insects discriminate colony members (nestmates/friends) from foreign workers (non-nestmates/foes) by colony-specific, multi-component colony odors. Traditionally, complex processing in the brain has been regarded as crucial for colony recognition. Odor information is represented as spatial patterns of activity and processed in the primary olfactory neuropile, the antennal lobe (AL) of insects, which is analogous to the vertebrate olfactory bulb. Correlative evidence indicates that the spatial activity patterns reflect odor-quality, i.e., how an odor is perceived. For colony odors, alternatively, a sensory filter in the peripheral nervous system was suggested, causing specific anosmia to nestmate colony odors. Here, we investigate neuronal correlates of colony odors in the brain of a social insect to directly test whether they are anosmic to nestmate colony odors and whether spatial activity patterns in the AL can predict how odor qualities like ‘‘friend’’ and ‘‘foe’’ are attributed to colony odors. Methodology/Principal Findings: Using ant dummies that mimic natural conditions, we presented colony odors and investigated their neuronal representation in the ant Camponotus floridanus. Nestmate and non-nestmate colony odors elicited neuronal activity: In the periphery, we recorded sensory responses of olfactory receptor neurons (electroantennography), and in the brain, we measured colony odor specific spatial activity patterns in the AL (calcium imaging). Surprisingly, upon repeated stimulation with the same colony odor, spatial activity patterns were variable, and as variable as activity patterns elicited by different colony odors. Conclusions: Ants are not anosmic to nestmate colony odors. However, spatial activity patterns in the AL alone do not provide sufficient information for colony odor discrimination and this finding challenges the current notion of how odor quality is coded. Our result illustrates the enormous challenge for the nervous system to classify multi-component odors and indicates that other neuronal parameters, e.g., precise timing of neuronal activity, are likely necessary for attribution of odor quality to multi-component odors

The Effect of Algorithms on Copy Number Variant Detection

BACKGROUND: The detection of copy number variants (CNVs) and the results of CNV-disease association studies rely on how CNVs are defined, and because array-based technologies can only infer CNVs, CNV-calling algorithms can produce vastly different findings. Several authors have noted the large-scale variability between CNV-detection methods, as well as the substantial false positive and false negative rates associated with those methods. In this study, we use variations of four common algorithms for CNV detection (PennCNV, QuantiSNP, HMMSeg, and cnvPartition) and two definitions of overlap (any overlap and an overlap of at least 40% of the smaller CNV) to illustrate the effects of varying algorithms and definitions of overlap on CNV discovery. METHODOLOGY AND PRINCIPAL FINDINGS: We used a 56 K Illumina genotyping array enriched for CNV regions to generate hybridization intensities and allele frequencies for 48 Caucasian schizophrenia cases and 48 age-, ethnicity-, and gender-matched control subjects. No algorithm found a difference in CNV burden between the two groups. However, the total number of CNVs called ranged from 102 to 3,765 across algorithms. The mean CNV size ranged from 46 kb to 787 kb, and the average number of CNVs per subject ranged from 1 to 39. The number of novel CNVs not previously reported in normal subjects ranged from 0 to 212. CONCLUSIONS AND SIGNIFICANCE: Motivated by the availability of multiple publicly available genome-wide SNP arrays, investigators are conducting numerous analyses to identify putative additional CNVs in complex genetic disorders. However, the number of CNVs identified in array-based studies, and whether these CNVs are novel or valid, will depend on the algorithm(s) used. Thus, given the variety of methods used, there will be many false positives and false negatives. Both guidelines for the identification of CNVs inferred from high-density arrays and the establishment of a gold standard for validation of CNVs are needed

How to Adapt to Changing Markets: Experience and Personality in a Repeated Investment Game

Investment behavior is traditionally investigated with the assumption that it is on average advantageous to invest. However, this may not always be the case. In this paper, we experimentally studied investment choices made by students and financial professionals facing alternately an advantageous and disadvantageous environment in a multi-round investment game. Expected returns from investment in the advantageous environment were higher than a safe alternative, while expected returns were lower in the disadvantageous environment. We investigate how experience and personality are related to choices. Investment behavior does not differ dependent on expected returns and professionals do not significantly differ from students. Personality predicts behavior and in particular we observe that openness to experience was an asset in unfavorable markets, leading to reduced risk taking

Classification of Dengue Fever Patients Based on Gene Expression Data Using Support Vector Machines

Background: Symptomatic infection by dengue virus (DENV) can range from dengue fever (DF) to dengue haemorrhagic fever (DHF), however, the determinants of DF or DHF progression are not completely understood. It is hypothesised that host innate immune response factors are involved in modulating the disease outcome and the expression levels of genes involved in this response could be used as early prognostic markers for disease severity. Methodology/Principal Findings: mRNA expression levels of genes involved in DENV innate immune responses were measured using quantitative real time PCR (qPCR). Here, we present a novel application of the support vector machines (SVM) algorithm to analyze the expression pattern of 12 genes in peripheral blood mononuclear cells (PBMCs) of 28 dengue patients (13 DHF and 15 DF) during acute viral infection. The SVM model was trained using gene expression data of these genes and achieved the highest accuracy of ,85% with leave-one-out cross-validation. Through selective removal of gene expression data from the SVM model, we have identified seven genes (MYD88, TLR7, TLR3, MDA5, IRF3, IFN-a and CLEC5A) that may be central in differentiating DF patients from DHF, with MYD88 and TLR7 observed to be the most important. Though the individual removal of expression data of five other genes had no impact on the overall accuracy, a significant combined role was observed when the SVM model of the two main genes (MYD88 and TLR7) was re-trained to include the five genes, increasing the overall accuracy to ,96%. Conclusions/Significance: Here, we present a novel use of the SVM algorithm to classify DF and DHF patients, as well as to elucidate the significance of the various genes involved. It was observed that seven genes are critical in classifying DF and DHF patients: TLR3, MDA5, IRF3, IFN-a, CLEC5A, and the two most important MYD88 and TLR7. While these preliminary results are promising, further experimental investigation is necessary to validate their specific roles in dengue disease