Trimming of mammalian transcriptional networks using network component analysis

<p>Abstract</p> <p>Background</p> <p>Network Component Analysis (NCA) has been used to deduce the activities of transcription factors (TFs) from gene expression data and the TF-gene binding relationship. However, the TF-gene interaction varies in different environmental conditions and tissues, but such information is rarely available and cannot be predicted simply by motif analysis. Thus, it is beneficial to identify key TF-gene interactions under the experimental condition based on transcriptome data. Such information would be useful in identifying key regulatory pathways and gene markers of TFs in further studies.</p> <p>Results</p> <p>We developed an algorithm to trim network connectivity such that the important regulatory interactions between the TFs and the genes were retained and the regulatory signals were deduced. Theoretical studies demonstrated that the regulatory signals were accurately reconstructed even in the case where only three independent transcriptome datasets were available. At least 80% of the main target genes were correctly predicted in the extreme condition of high noise level and small number of datasets. Our algorithm was tested with transcriptome data taken from mice under rapamycin treatment. The initial network topology from the literature contains 70 TFs, 778 genes, and 1423 edges between the TFs and genes. Our method retained 1074 edges (i.e. 75% of the original edge number) and identified 17 TFs as being significantly perturbed under the experimental condition. Twelve of these TFs are involved in MAPK signaling or myeloid leukemia pathways defined in the KEGG database, or are known to physically interact with each other. Additionally, four of these TFs, which are Hif1a, Cebpb, Nfkb1, and Atf1, are known targets of rapamycin. Furthermore, the trimmed network was able to predict <it>Eno1 </it>as an important target of Hif1a; this key interaction could not be detected without trimming the regulatory network.</p> <p>Conclusions</p> <p>The advantage of our new algorithm, relative to the original NCA, is that our algorithm can identify the important TF-gene interactions. Identifying the important TF-gene interactions is crucial for understanding the roles of pleiotropic global regulators, such as p53. Also, our algorithm has been developed to overcome NCA's inability to analyze large networks where multiple TFs regulate a single gene. Thus, our algorithm extends the applicability of NCA to the realm of mammalian regulatory network analysis.</p

Gender dimorphism and age of onset in malignant peripheral nerve sheath tumor preclinical models and human patients.

BackgroundGender-based differences in disease onset in murine models of malignant peripheral nerve sheath tumor (MPNST) and in patients with Neurofibromatosis type-1-(NF-1)-associated or spontaneous MPNST has not been well studied.MethodsForty-three mGFAP-Cre+;Ptenloxp/+;LSL-K-rasG12D/+ mice were observed for tumor development and evaluated for gender disparity in age of MPNST onset. Patient data from the prospectively collected UCLA sarcoma database (1974-2011, n = 113 MPNST patients) and 39 published studies on MPNST patients (n = 916) were analyzed for age of onset differences between sexes and between NF-1 and spontaneous MPNST patients.ResultsOur murine model showed gender-based differences in MPNST onset, with males developing MPNST significantly earlier than females (142 vs. 162 days, p = 0.015). In the UCLA patient population, males also developed MPNST earlier than females (median age 35 vs. 39.5 years, p = 0.048). Patients with NF-1-associated MPNST had significantly earlier age of onset compared to spontaneous MPNST (median age 33 vs. 39 years, p = 0.007). However, expanded analysis of 916 published MPNST cases revealed no significant age difference in MPNST onset between males and females. Similar to the UCLA dataset, patients with NF-1 developed MPNST at a significantly younger age than spontaneous MPNST patients (p &lt; 0.0001, median age 28 vs. 41 years) and this disparity was maintained across North American, European, and Asian populations.ConclusionsAlthough our preclinical model and single-institution patient cohort show gender dimorphism in MPNST onset, no significant gender disparity was detected in the larger MPNST patient meta-dataset. NF-1 patients develop MPNST 13 years earlier than patients with spontaneous MPNST, with little geographical variance

Inferring causal genomic alterations in breast cancer using gene expression data

<p>Abstract</p> <p>Background</p> <p>One of the primary objectives in cancer research is to identify causal genomic alterations, such as somatic copy number variation (CNV) and somatic mutations, during tumor development. Many valuable studies lack genomic data to detect CNV; therefore, methods that are able to infer CNVs from gene expression data would help maximize the value of these studies.</p> <p>Results</p> <p>We developed a framework for identifying recurrent regions of CNV and distinguishing the cancer driver genes from the passenger genes in the regions. By inferring CNV regions across many datasets we were able to identify 109 recurrent amplified/deleted CNV regions. Many of these regions are enriched for genes involved in many important processes associated with tumorigenesis and cancer progression. Genes in these recurrent CNV regions were then examined in the context of gene regulatory networks to prioritize putative cancer driver genes. The cancer driver genes uncovered by the framework include not only well-known oncogenes but also a number of novel cancer susceptibility genes validated via siRNA experiments.</p> <p>Conclusions</p> <p>To our knowledge, this is the first effort to systematically identify and validate drivers for expression based CNV regions in breast cancer. The framework where the wavelet analysis of copy number alteration based on expression coupled with the gene regulatory network analysis, provides a blueprint for leveraging genomic data to identify key regulatory components and gene targets. This integrative approach can be applied to many other large-scale gene expression studies and other novel types of cancer data such as next-generation sequencing based expression (RNA-Seq) as well as CNV data.</p

Novel Dedifferentiated Liposarcoma Xenograft Models Reveal PTEN Down-Regulation as a Malignant Signature and Response to PI3K Pathway Inhibition

Liposarcoma is a type of soft tissue sarcoma that exhibits poor survival and a high recurrence rate. Treatment is generally limited to surgery and radiation, which emphasizes the need for better understanding of this disease. Because very few in vivo and in vitro models can reproducibly recapitulate the human disease, we generated several xenograft models from surgically resected human dedifferentiated liposarcoma. All xenografts recapitulated morphological and gene expression characteristics of the patient tumors after continuous in vivo passages. Importantly, xenograftability was directly correlated with disease-specific survival of liposarcoma patients. Thus, the ability for the tumor of a patient to engraft may help identify those patients who will benefit from more aggressive treatment regimens. Gene expression analyses highlighted the association between xenograftability and a unique gene expression signature, including down-regulated PTEN tumor-suppressor gene expression and a progenitor-like phenotype. When treated with the PI3K/AKT/mTOR pathway inhibitor rapamycin alone or in combination with the multikinase inhibitor sorafenib, all xenografts responded with increased lipid content and a more differentiated gene expression profile. These human xenograft models may facilitate liposarcoma research and accelerate the generation of readily translatable preclinical data that could ultimately influence patient care

Experimental studies with two novel silicon detectors for the development of time-of-flight spectrometry of laser-accelerated proton beams

Laser-accelerated proton beams exhibit remarkably different beam characteristics as compared to conventionally accelerated ion beams. About 105 to 107 particles per MeV and msr are accelerated quasi-instantaneously within about 1 ps. The resulting energy spectrum typically shows an exponentially decaying distribution. Our planned approach to determine the energy spectrum of the particles generated in each pulse is to exploit the time-of-flight (TOF) difference of protons with different kinetic energies at 1 m distance from the laser-target interaction. This requires fast and sensitive detectors. We therefore tested two prototype silicon detectors, developed at the Centre for Medical Radiation Physics at the University of Wollongong with a current amplifier, regarding their suitability for TOF-spectrometry in terms of sensitivity and timing properties. For the latter, we illuminated the detectors with short laser pulses, measured the signal current and compared it to the signal of a fast photodiode. The comparison revealed that the timing properties of both prototypes are not yet sufficient for our purpose. In contrast, our results regarding the detectors\u27 sensitivity are promising. The lowest detectable proton flux at 10 MeV was found to be 25 protons per ns on the detector. With this sensitivity and with a smaller pixelation of the detectors, the timing properties can be improved for new prototypes, making them potential candidates for TOF-spectrometry of laser-accelerated particle beams

Network Coding with Multimedia Transmission and Cognitive Networking: An Implementation based on Software-Defined Radio

Network coding (NC) is considered a breakthrough to improve throughput, robustness, and security of wireless networks. Although the theoretical aspects of NC have been extensively investigated, there have been only few experiments with pure NC schematics. This paper presents an implementation of NC under a two-way relay model and extends it to two\ua0non-straightforward scenarios: (i) multimedia transmission with layered coding and multiple-description coding, and (ii) cognitive radio with Vandermonde frequency division multiplexing (VFDM). The implementation is in real time and based on software-defined radio (SDR). The experimental results show that, by combining NC and source coding, we can control the quality of the received multimedia content in an on-demand manner. Whereas in the VFDM-based cognitive radio, the quality of the received content in the primary receiver is low (due to imperfect channel estimation) yet retrievable. Our implementation results serve as a proof for the practicability of network coding in relevant applications

3D silicon microdosimetry and RBE study using C-12 ion of different energies

This paper presents a new version of the 3D mesa "bridge" microdosimeter comprised of an array of 4248 silicon cells fabricated on 10 µm thick silicon-on-insulator substrate. This microdosimeter has been designed to overcome limitations existing in previous generation silicon microdosimeters and it provides well-defined sensitive volumes and high spatial resolution. The charge collection characteristics of the new 3D mesa microdosimeter were investigated using the ANSTO heavy ion microprobe, utilizing 5.5 MeV He2+ ions. Measurement of microdosimetric quantities allowed for the determination of the Relative Biological Effectiveness of 290 MeV/u and 350 MeV/u 12C heavy ion therapy beams at the Heavy Ion Medical Accelerator in Chiba (HIMAC), Japan. The microdosimetric RBE obtained showed good agreement with the tissue-equivalent proportional counter. Utilizing the high spatial resolution of the SOI microdosimeter, the LET spectra for 70 MeV 12C+6 ions, like those present at the distal edge of 290 and 350 MeV/u beams, were obtained as the ions passed through thin layers of polyethylene film. This microdosimeter can provide useful information about the lineal energy transfer (LET) spectra downstream of the protective layers used for shielding of electronic devices for single event upset prediction

Pichia pastoris versus Saccharomyces cerevisiae:a case study on the recombinant production of human granulocyte-macrophage colony-stimulating factor

BACKGROUND: Recombinant human granulocyte-macrophage colony-stimulating factor (rhGM-CSF) is a glycoprotein that has been approved by the FDA for the treatment of neutropenia and leukemia in combination with chemotherapies. Recombinant hGM-CSF is produced industrially using the baker's yeast, Saccharomyces cerevisiae, by large-scale fermentation. The methylotrophic yeast, Pichia pastoris, has emerged as an alternative host cell system due to its shorter and less immunogenic glycosylation pattern together with higher cell density growth and higher secreted protein yield than S. cerevisiae. In this study, we compared the pipeline from gene to recombinant protein in these two yeasts. RESULTS: Codon optimization in silico for both yeast species showed no difference in frequent codon usage. However, rhGM-CSF expressed from S. cerevisiae BY4742 showed a significant discrepancy in molecular weight from those of P. pastoris X33. Analysis showed purified rhGM-CSF species with molecular weights ranging from 30 to more than 60 kDa. Fed-batch fermentation over 72 h showed that rhGM-CSF was more highly secreted from P. pastoris than S. cerevisiae (285 and 64 mg total secreted protein/L, respectively). Ion exchange chromatography gave higher purity and recovery than hydrophobic interaction chromatography. Purified rhGM-CSF from P. pastoris was 327 times more potent than rhGM-CSF from S. cerevisiae in terms of proliferative stimulating capacity on the hGM-CSF-dependent cell line, TF-1. CONCLUSION: Our data support a view that the methylotrophic yeast P. pastoris is an effective recombinant host for heterologous rhGM-CSF production