50 research outputs found
The vascular bone marrow niche influences outcome in chronic myeloid leukemia via the E-selectin - SCL/TAL1-CD44 axis.
The endosteal bone marrow niche and vascular endothelial cells provide sanctuaries for leukemic cells. In murine chronic myeloid leukemia (CML) CD44 on leukemia cells and E-selectin on bone marrow endothelium are essential mediators for the engraftment of leukemic stem cells. We hypothesized that non-adhesion of CML-initiating cells to E-selectin on the bone marrow endothelium may lead to superior eradication of leukemic stem cells in CML after treatment with imatinib than imatinib alone. Indeed, here we show that treatment with the E-selectin inhibitor GMI-1271 in combination with imatinib prolongs survival of mice with CML via decreased contact time of leukemia cells with bone marrow endothelium. Non-adhesion of BCR-ABL1(+) cells leads to an increase of cell cycle progression and an increase of expression of the hematopoietic transcription factor and proto-oncogene Scl/Tal1 in leukemia-initiating cells. We implicate SCL/TAL1 as an indirect phosphorylation target of BCR-ABL1 and as a negative transcriptional regulator of CD44 expression. We show that increased SCL/TAL1 expression is associated with improved outcome in human CML. These data demonstrate the BCR-ABL1-specific, cell-intrinsic pathways leading to altered interactions with the vascular niche via the modulation of adhesion molecules - which could be exploited therapeutically in the future
Fast network centrality analysis using GPUs
<p>Abstract</p> <p>Background</p> <p>With the exploding volume of data generated by continuously evolving high-throughput technologies, biological network analysis problems are growing larger in scale and craving for more computational power. General Purpose computation on Graphics Processing Units (GPGPU) provides a cost-effective technology for the study of large-scale biological networks. Designing algorithms that maximize data parallelism is the key in leveraging the power of GPUs.</p> <p>Results</p> <p>We proposed an efficient data parallel formulation of the All-Pairs Shortest Path problem, which is the key component for shortest path-based centrality computation. A betweenness centrality algorithm built upon this formulation was developed and benchmarked against the most recent GPU-based algorithm. Speedup between 11 to 19% was observed in various simulated scale-free networks. We further designed three algorithms based on this core component to compute closeness centrality, eccentricity centrality and stress centrality. To make all these algorithms available to the research community, we developed a software package <it>gpu</it>-<it>fan </it>(GPU-based Fast Analysis of Networks) for CUDA enabled GPUs. Speedup of 10-50Ă compared with CPU implementations was observed for simulated scale-free networks and real world biological networks.</p> <p>Conclusions</p> <p><it>gpu</it>-<it>fan </it>provides a significant performance improvement for centrality computation in large-scale networks. Source code is available under the GNU Public License (GPL) at <url>http://bioinfo.vanderbilt.edu/gpu-fan/</url>.</p
CUDASW++2.0: enhanced Smith-Waterman protein database search on CUDA-enabled GPUs based on SIMT and virtualized SIMD abstractions
<p>Abstract</p> <p>Background</p> <p>Due to its high sensitivity, the Smith-Waterman algorithm is widely used for biological database searches. Unfortunately, the quadratic time complexity of this algorithm makes it highly time-consuming. The exponential growth of biological databases further deteriorates the situation. To accelerate this algorithm, many efforts have been made to develop techniques in high performance architectures, especially the recently emerging many-core architectures and their associated programming models.</p> <p>Findings</p> <p>This paper describes the latest release of the CUDASW++ software, CUDASW++ 2.0, which makes new contributions to Smith-Waterman protein database searches using compute unified device architecture (CUDA). A parallel Smith-Waterman algorithm is proposed to further optimize the performance of CUDASW++ 1.0 based on the single instruction, multiple thread (SIMT) abstraction. For the first time, we have investigated a partitioned vectorized Smith-Waterman algorithm using CUDA based on the virtualized single instruction, multiple data (SIMD) abstraction. The optimized SIMT and the partitioned vectorized algorithms were benchmarked, and remarkably, have similar performance characteristics. CUDASW++ 2.0 achieves performance improvement over CUDASW++ 1.0 as much as 1.74 (1.72) times using the optimized SIMT algorithm and up to 1.77 (1.66) times using the partitioned vectorized algorithm, with a performance of up to 17 (30) billion cells update per second (GCUPS) on a single-GPU GeForce GTX 280 (dual-GPU GeForce GTX 295) graphics card.</p> <p>Conclusions</p> <p>CUDASW++ 2.0 is publicly available open-source software, written in CUDA and C++ programming languages. It obtains significant performance improvement over CUDASW++ 1.0 using either the optimized SIMT algorithm or the partitioned vectorized algorithm for Smith-Waterman protein database searches by fully exploiting the compute capability of commonly used CUDA-enabled low-cost GPUs.</p
Therapeutic potential of KLF2-induced exosomal microRNAs in pulmonary hypertension
Pulmonary arterial hypertension (PAH) is a severe disorder of lung vasculature that causes right heart failure. Homeostatic effects of flow-activated transcription factor KrĂźppel-like factor 2 (KLF2) are compromised in PAH. Here we show that KLF2-induced exosomal microRNAs, miR-181a-5p and miR-324-5p act together to attenuate pulmonary vascular remodeling and that their actions are mediated by Notch4 and ETS1 and other key regulators of vascular homeostasis. Expressions of KLF2, miR-181a-5p and miR-324-5p are reduced, while levels of their target genes are elevated in pre-clinical PAH, idiopathic PAH and heritable PAH with missense p.H288Y KLF2 mutation. Therapeutic supplementation of miR-181a-5p and miR-324-5p reduces proliferative and angiogenic responses in patient-derived cells and attenuates disease progression in PAH mice. This study shows that reduced KLF2 signaling is a common feature of human PAH and highlights the potential therapeutic role of KLF2-regulated exosomal miRNAs in PAH and other diseases associated with vascular remodelling
Accelerating pairwise statistical significance estimation for local alignment by harvesting GPU's power
An efficient algorithm for systematic analysis of nucleotide strings suitable for siRNA design
<p>Abstract</p> <p>Background</p> <p>The "off-target" silencing effect hinders the development of siRNA-based therapeutic and research applications. Existing solutions for finding possible locations of siRNA seats within a large database of genes are either too slow, miss a portion of the targets, or are simply not designed to handle a very large number of queries. We propose a new approach that reduces the computational time as compared to existing techniques.</p> <p>Findings</p> <p>The proposed method employs tree-based storage in a form of a modified truncated suffix tree to sort all possible short string substrings within given set of strings (i.e. transcriptome). Using the new algorithm, we pre-computed a list of the best siRNA locations within each human gene ("siRNA seats"). siRNAs designed to reside within siRNA seats are less likely to hybridize off-target. These siRNA seats could be used as an input for the traditional "set-of-rules" type of siRNA designing software. The list of siRNA seats is available through a publicly available database located at <url>http://web.cos.gmu.edu/~gmanyam/siRNA_db/search.php</url></p> <p>Conclusions</p> <p>In attempt to perform top-down prediction of the human siRNA with minimized off-target hybridization, we developed an efficient algorithm that employs suffix tree based storage of the substrings. Applications of this approach are not limited to optimal siRNA design, but can also be useful for other tasks involving selection of the characteristic strings specific to individual genes. These strings could then be used as siRNA seats, as specific probes for gene expression studies by oligonucleotide-based microarrays, for the design of molecular beacon probes for Real-Time PCR and, generally, any type of PCR primers.</p
On the Virtualization of CUDA Based GPU Remoting on ARM and X86 Machines in the GVirtuS Framework
Fast and accurate protein substructure searching with simulated annealing and GPUs
<p>Abstract</p> <p>Background</p> <p>Searching a database of protein structures for matches to a query structure, or occurrences of a structural motif, is an important task in structural biology and bioinformatics. While there are many existing methods for structural similarity searching, faster and more accurate approaches are still required, and few current methods are capable of substructure (motif) searching.</p> <p>Results</p> <p>We developed an improved heuristic for tableau-based protein structure and substructure searching using simulated annealing, that is as fast or faster and comparable in accuracy, with some widely used existing methods. Furthermore, we created a parallel implementation on a modern graphics processing unit (GPU).</p> <p>Conclusions</p> <p>The GPU implementation achieves up to 34 times speedup over the CPU implementation of tableau-based structure search with simulated annealing, making it one of the fastest available methods. To the best of our knowledge, this is the first application of a GPU to the protein structural search problem.</p