News Article | November 23, 2016
PHOENIX, Ariz. -- Nov. 23, 2016 -- Faster and more precise information about how best to treat cancer patients should be possible thanks to a $200,000 Compute the Cure grant announced today from the NVIDIA Foundation to the Translational Genomics Research Institute (TGen). The grant will help TGen accelerate the computer processing of transcriptomes from thousands of cells gleaned from patient tumor samples, using a complex computational algorithm. Transcriptomes are all the messenger RNA (mRNA) molecules expressed from an individual's genes. This process will advance the practice of precision medicine by quickly informing doctors with the best options for attacking each individual patient's cancer. "When you analyze actual patient data, your goal is to help physicians better understand treatment options, providing these answers to the doctors as soon as you possibly can. There is a lot at stake for our patients, and time is critical," said Dr. Seungchan Kim, an Associate Professor and head of TGen's Biocomputing Unit. Specifically, the grant will enable TGen to perfect its prototype statistical analysis tool called EDDY (evaluation of differential dependency), reducing its analysis turn-around from months to days. By simultaneously sequencing the mRNAs of thousands of individual cancer cells from the same tumor, it will help physicians understand why some cancer cells respond to treatment, and some don't, leading to more precisely targeted therapeutics. "We can actually separate all these individual cells and then look in more fine detail at how each cell responds to each compound," Dr. Kim said. "With that information we can propose which treatment might be best for each patient. You might have to use more than one compound to get all the tumor cells." According to TGen's project proposal, single-cell RNA sequencing addresses several shortcomings of the traditional averaging of RNA expression from multiple cells. In isolating the specific genetic profile of individual cells, subtle changes in biological behavior are brought into sharp focus, enabling new research directions such as microevolution, dynamic RNA processes and the biological mechanisms involved in rare diseases. The challenge for researchers is that each cell contains billions of pieces of genetic information. Initial attempts to simultaneously analyze thousands of tumor cells proved time consuming. Using a CPU -- a central processing unit, which is designed to conduct many different tasks -- EDDY ran for two months and was still not able to complete the analysis of an initial batch of more than 4,700 samples, even using hundreds of CPUs simultaneously. However, using a GPU -- a graphics processing unit created by NVIDIA, which is designed to accomplish simple tasks but in massive parallel computing units -- researchers anticipate EDDY will be able to analyze thousands of samples in a matter of days. Working with the University of California San Francisco, TGen will apply this GPU-accelerated process to a study of brain cancer patients, analyzing their tumors, proposing therapies and monitoring the results. "That is the promise of this grant proposal," said Dr. Harshil Dhruv, an Assistant Professor in TGen's Cancer and Cell Biology Division. "With the GPU, we can speed up the computation significantly, process the patient data within a few days, and give that information back to the oncologists so they can make an informed decision about how best to help the patient." Compute the Cure is the NVIDIA Foundation's philanthropic initiative to fund computational efforts to advance cancer research, diagnostics and treatment, support non-profits that provide patient care and support services, and engage its employees in fundraising activities. Through this initiative, the NVIDIA Foundation has donated nearly $3 million to cancer causes since 2011. The award to TGen was selected by a group of NVIDIA employees, with the support of researchers at the National Cancer Institute, from among nearly 20 proposals submitted from across the globe. "Advanced computation is indispensable to the search for cancer cures, so we're supporting researchers who embrace this view, like TGen's Dr. Kim. We're impressed with his novel use of single-cell transcriptomic profiling, the broad experience of his research team, and the potential of his GPU-accelerated analysis method to advance the clinical practice of precision medicine in cancer," said John Montrym, chief architect at NVIDIA, and an NVIDIA Foundation Compute the Cure review committee member. Translational Genomics Research Institute (TGen) is a Phoenix, Arizona-based non-profit organization dedicated to conducting groundbreaking research with life changing results. TGen is focused on helping patients with neurological disorders, cancer, and diabetes, through cutting edge translational research (the process of rapidly moving research towards patient benefit). TGen physicians and scientists work to unravel the genetic components of both common and rare complex diseases in adults and children. Working with collaborators in the scientific and medical communities literally worldwide, TGen makes a substantial contribution to help our patients through efficiency and effectiveness of the translational process. For more information, visit: http://www. . Follow TGen on Facebook, LinkedIn and Twitter @TGen.
PubMed | Immunology and Oncology and., University of Lausanne, French Institute of Health and Medical Research, Cell Division and Cancer Group and Biocomputing Unit
Type: Journal Article | Journal: Proceedings of the National Academy of Sciences of the United States of America | Year: 2015
Neutrophils are antigen-transporting cells that generate vaccinia virus (VACV)-specific T-cell responses, yet how VACV modulates neutrophil recruitment and its significance in the immune response are unknown. We generated an attenuated VACV strain that expresses HIV-1 clade C antigens but lacks three specific viral genes (A52R, K7R, and B15R). We found that these genes act together to inhibit the NFB signaling pathway. Triple ablation in modified virus restored NFB function in macrophages. After virus infection of mice, NFB pathway activation led to expression of several cytokines/chemokines that increased the migration of neutrophil populations (N and N) to the infection site. N cells displayed features of antigen-presenting cells and activated virus-specific CD8 T cells. Enhanced neutrophil trafficking to the infection site correlated with an increased T-cell response to HIV vector-delivered antigens. These results identify a mechanism for poxvirus-induced immune response and alternatives for vaccine vector design.
Cecil A.,University of Würzburg |
Ohlsen K.,University of Würzburg |
Menzel T.,University of Würzburg |
Francois P.,University of Geneva |
And 17 more authors.
International Journal of Medical Microbiology | Year: 2015
Isoquinolines (IQs) are natural substances with an antibiotic potential we aim to optimize. Specifically, IQ-238 is a synthetic analog of the novel-type N,. C-coupled naphthylisoquinoline (NIQ) alkaloid ancisheynine. Recently, we developed and tested other IQs such as IQ-143. By utilizing genome-wide gene expression data, metabolic network modelling and Voronoi tessalation based data analysis - as well as cytotoxicity measurements, chemical properties calculations and principal component analysis of the NIQs - we show that IQ-238 has strong antibiotic potential for staphylococci and low cytotoxicity against murine or human cells. Compared to IQ-143, systemic effects are less pronounced. Most enzyme activity changes due to IQ-238 are located in the carbohydrate metabolism. Validation includes metabolite measurements on biological replicates. IQ-238 delineates key properties and a chemical space for a good therapeutic window. The combination of analysis methods allows suggestions for further lead development and yields an in-depth look at staphylococcal adaptation and network changes after antibiosis. Results are compared to eukaryotic host cells. © 2014 Elsevier GmbH.
Cecil A.,University of Würzburg |
Rikanovic C.,University of Würzburg |
Ohlsen K.,University of Würzburg |
Liang C.,University of Würzburg |
And 9 more authors.
Genome Biology | Year: 2011
Background: Xenobiotics represent an environmental stress and as such are a source for antibiotics, including the isoquinoline (IQ) compound IQ-143. Here, we demonstrate the utility of complementary analysis of both host and pathogen datasets in assessing bacterial adaptation to IQ-143, a synthetic analog of the novel type N,C-coupled naphthyl-isoquinoline alkaloid ancisheynine.Results: Metabolite measurements, gene expression data and functional assays were combined with metabolic modeling to assess the effects of IQ-143 on Staphylococcus aureus, Staphylococcus epidermidis and human cell lines, as a potential paradigm for novel antibiotics. Genome annotation and PCR validation identified novel enzymes in the primary metabolism of staphylococci. Gene expression response analysis and metabolic modeling demonstrated the adaptation of enzymes to IQ-143, including those not affected by significant gene expression changes. At lower concentrations, IQ-143 was bacteriostatic, and at higher concentrations bactericidal, while the analysis suggested that the mode of action was a direct interference in nucleotide and energy metabolism. Experiments in human cell lines supported the conclusions from pathway modeling and found that IQ-143 had low cytotoxicity.Conclusions: The data suggest that IQ-143 is a promising lead compound for antibiotic therapy against staphylococci. The combination of gene expression and metabolite analyses with in silico modeling of metabolite pathways allowed us to study metabolic adaptations in detail and can be used for the evaluation of metabolic effects of other xenobiotics. © 2011 Cecil et al.; licensee BioMed Central Ltd.
Kunz M.,Functional Genomics and Systems Biology Group |
Kunz M.,Hannover Medical School |
Xiao K.,Hannover Medical School |
Xiao K.,University of Kiel |
And 8 more authors.
Journal of Molecular and Cellular Cardiology | Year: 2015
MicroRNAs (miRNAs) are small ~ 22 nucleotide non-coding RNAs and are highly conserved among species. Moreover, miRNAs regulate gene expression of a large number of genes associated with important biological functions and signaling pathways. Recently, several miRNAs have been found to be associated with cardiovascular diseases. Thus, investigating the complex regulatory effect of miRNAs may lead to a better understanding of their functional role in the heart. To achieve this, bioinformatics approaches have to be coupled with validation and screening experiments to understand the complex interactions of miRNAs with the genome. This will boost the subsequent development of diagnostic markers and our understanding of the physiological and therapeutic role of miRNAs in cardiac remodeling. In this review, we focus on and explain different bioinformatics strategies and algorithms for the identification and analysis of miRNAs and their regulatory elements to better understand cardiac miRNA biology. Starting with the biogenesis of miRNAs, we present approaches such as LocARNA and miRBase for combining sequence and structure analysis including phylogenetic comparisons as well as detailed analysis of RNA folding patterns, functional target prediction, signaling pathway as well as functional analysis. We also show how far bioinformatics helps to tackle the unprecedented level of complexity and systemic effects by miRNA, underlining the strong therapeutic potential of miRNA and miRNA target structures in cardiovascular disease. In addition, we discuss drawbacks and limitations of bioinformatics algorithms and the necessity of experimental approaches for miRNA target identification. This article is part of a Special Issue entitled 'Non-coding RNAs'. © 2014 Elsevier Ltd.
Gupta S.K.,University of Würzburg |
Kupper M.,University of Würzburg |
Ratzka C.,University of Würzburg |
Feldhaar H.,University of Bayreuth |
And 5 more authors.
BMC Genomics | Year: 2015
Background: Defence mechanisms of organisms are shaped by their lifestyle, environment and pathogen pressure. Carpenter ants are social insects which live in huge colonies comprising genetically closely related individuals in high densities within nests. This lifestyle potentially facilitates the rapid spread of pathogens between individuals. In concert with their innate immune system, social insects may apply external immune defences to manipulate the microbial community among individuals and within nests. Additionally, carpenter ants carry a mutualistic intracellular and obligate endosymbiotic bacterium, possibly maintained and regulated by the innate immune system. Thus, different selective forces could shape internal immune defences of Camponotus floridanus. Results: The immune gene repertoire of C. floridanus was investigated by re-evaluating its genome sequence combined with a full transcriptome analysis of immune challenged and control animals using Illumina sequencing. The genome was re-annotated by mapping transcriptome reads and masking repeats. A total of 978 protein sequences were characterised further by annotating functional domains, leading to a change in their original annotation regarding function and domain composition in about 8 % of all proteins. Based on homology analysis with key components of major immune pathways of insects, the C. floridanus immune-related genes were compared to those of Drosophila melanogaster, Apis mellifera, and other hymenoptera. This analysis revealed that overall the immune system of carpenter ants comprises many components found in these insects. In addition, several C. floridanus specific genes of yet unknown functions but which are strongly induced after immune challenge were discovered. In contrast to solitary insects like Drosophila or the hymenopteran Nasonia vitripennis, the number of genes encoding pattern recognition receptors specific for bacterial peptidoglycan (PGN) and a variety of known antimicrobial peptide (AMP) genes is lower in C. floridanus. The comparative analysis of gene expression post immune-challenge in different developmental stages of C. floridanus suggests a stronger induction of immune gene expression in larvae in comparison to adults. Conclusions: The comparison of the immune system of C. floridanus with that of other insects revealed the presence of a broad immune repertoire. However, the relatively low number of PGN recognition proteins and AMPs, the identification of Camponotus specific putative immune genes, and stage specific differences in immune gene regulation reflects Camponotus specific evolution including adaptations to its lifestyle. © 2015 Gupta et al.
Zhang W.,South China Normal University |
Lu X.,South China Normal University |
Luo C.,South China Normal University |
Zhong L.,South China Normal University |
Vargas J.,Biocomputing Unit
Optics Communications | Year: 2015
Combing a sequence of simultaneous phase-shifting dual-wavelength interferograms (SPSDWIs) and principal component analysis (PCA) algorithm, we propose a novel phase retrieval approach in dual-wavelength interferometry. Firstly, for each wavelength, two mutually orthogonal principal component maps are constructed from a sequence of SPSDWIs through using the PCA algorithm, in which SPSDWIs are captured using a monochrome CCD and random and unknown phase shifts. Subsequently, the wrapped phases of each wavelength are obtained directly from the two orthogonal maps by performing the arctangent operation. Finally, an unambiguous phase of an extended synthetic beat wavelength is determined by a simple subtraction between these two wrapped phases. Both, the simulation and the experimental results demonstrate that the proposed approach reveals the simple and convenient performance, faster computing speed and good accuracy. © 2014 Elsevier B.V.
Gupta S.K.,Biocenter |
Gross R.,Biocenter |
Dandekar T.,Biocenter |
Dandekar T.,BioComputing Unit
Gene | Year: 2016
We investigate a drug target screening pipeline comparing sequence, structure and network-based criteria for prioritization. Serratia marcescens, an opportunistic pathogen, serves as test case. We rank according to (i) availability of three dimensional structures and lead compounds, (ii) not occurring in man and general sequence conservation information, and (iii) network information on the importance of the protein (conserved protein-protein interactions; metabolism; reported to be an essential gene in other organisms). We identify 45 potential anti-microbial drug targets in S. marcescens with KdsA involved in LPS biosynthesis as top candidate drug target. LpxC and FlgB are further top-ranked targets identified by interactome analysis not suggested before for S. marcescens. Pipeline, targets and complementarity of the three approaches are evaluated by available experimental data and genetic evidence and against other antibiotic screening pipelines. This supports reliable drug target identification and prioritization for infectious agents (bacteria, parasites, fungi) by these bundled complementary criteria. © 2016 Elsevier B.V.
PubMed | Biocenter and BioComputing Unit
Type: Journal Article | Journal: Gene | Year: 2016
We investigate a drug target screening pipeline comparing sequence, structure and network-based criteria for prioritization. Serratia marcescens, an opportunistic pathogen, serves as test case. We rank according to (i) availability of three dimensional structures and lead compounds, (ii) not occurring in man and general sequence conservation information, and (iii) network information on the importance of the protein (conserved protein-protein interactions; metabolism; reported to be an essential gene in other organisms). We identify 45 potential anti-microbial drug targets in S. marcescens with KdsA involved in LPS biosynthesis as top candidate drug target. LpxC and FlgB are further top-ranked targets identified by interactome analysis not suggested before for S. marcescens. Pipeline, targets and complementarity of the three approaches are evaluated by available experimental data and genetic evidence and against other antibiotic screening pipelines. This supports reliable drug target identification and prioritization for infectious agents (bacteria, parasites, fungi) by these bundled complementary criteria.