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

CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment

Background Searching for similarities in protein and DNA databases has become a routine procedure in Molecular Biology. The Smith-Waterman algorithm has been available for more than 25 years. It is based on a dynamic programming approach that explores all the possible alignments between two sequences; as a result it returns the optimal local alignment. Unfortunately, the computational cost is very high, requiring a number of operations proportional to the product of the length of two sequences. Furthermore, the exponential growth of protein and DNA databases makes the Smith-Waterman algorithm unrealistic for searching similarities in large sets of sequences. For these reasons heuristic approaches such as those implemented in FASTA and BLAST tend to be preferred, allowing faster execution times at the cost of reduced sensitivity. The main motivation of our work is to exploit the huge computational power of commonly available graphic cards, to develop high performance solutions for sequence alignment. Results In this paper we present what we believe is the fastest solution of the exact Smith-Waterman algorithm running on commodity hardware. It is implemented in the recently released CUDA programming environment by NVidia. CUDA allows direct access to the hardware primitives of the last-generation Graphics Processing Units (GPU) G80. Speeds of more than 3.5 GCUPS (Giga Cell Updates Per Second) are achieved on a workstation running two GeForce 8800 GTX. Exhaustive tests have been done to compare our implementation to SSEARCH and BLAST, running on a 3 GHz Intel Pentium IV processor. Our solution was also compared to a recently published GPU implementation and to a Single Instruction Multiple Data (SIMD) solution. These tests show that our implementation performs from 2 to 30 times faster than any other previous attempt available on commodity hardware. Conclusions The results show that graphic cards are now sufficiently advanced to be used as efficient hardware accelerators for sequence alignment. Their performance is better than any alternative available on commodity hardware platforms. The solution presented in this paper allows large scale alignments to be performed at low cost, using the exact Smith-Waterman algorithm instead of the largely adopted heuristic approaches

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 10-50× 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

Performance Analysis of the SHA-3 Candidates on Exotic Multi-core Architectures

The NIST hash function competition to design a new cryptographic hash standard 'SHA-3' is currently one of the hot topics in cryptologic research, its outcome heavily depends on the public evaluation of the remaining 14 candidates. There have been several cryptanalytic efforts to evaluate the security of these hash functions. Concurrently, invaluable benchmarking efforts have been made to measure the performance of the candidates on multiple architectures. In this paper we contribute to the latter; we evaluate the performance of all second-round SHA-3 candidates on two exotic platforms: the Cell Broadband Engine (Cell) and the NVIDIA Graphics Processing Units (GPUs). Firstly, we give performance estimates for each candidate based on the number of arithmetic instructions, which can be used as a starting point for evaluating the performance of the SHA-3 candidates on various platforms. Secondly, we use these generic estimates and Cell-/GPU-specific optimization techniques to give more precise figures for our target platforms, and finally, we present implementation results of all 10 non-AES based SHA-3 candidates

Single cell sequencing reveals endothelial plasticity with transient mesenchymal activation after myocardial infarction.

Endothelial cells play a critical role in the adaptation of tissues to injury. Tissue ischemia induced by infarction leads to profound changes in endothelial cell functions and can induce transition to a mesenchymal state. Here we explore the kinetics and individual cellular responses of endothelial cells after myocardial infarction by using single cell RNA sequencing. This study demonstrates a time dependent switch in endothelial cell proliferation and inflammation associated with transient changes in metabolic gene signatures. Trajectory analysis reveals that the majority of endothelial cells 3 to 7 days after myocardial infarction acquire a transient state, characterized by mesenchymal gene expression, which returns to baseline 14 days after injury. Lineage tracing, using the Cdh5-CreERT2;mT/mG mice followed by single cell RNA sequencing, confirms the transient mesenchymal transition and reveals additional hypoxic and inflammatory signatures of endothelial cells during early and late states after injury. These data suggest that endothelial cells undergo a transient mes-enchymal activation concomitant with a metabolic adaptation within the first days after myocardial infarction but do not acquire a long-term mesenchymal fate. This mesenchymal activation may facilitate endothelial cell migration and clonal expansion to regenerate the vascular network

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 Krüppel-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

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

CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment

Background Searching for similarities in protein and DNA databases has become a routine procedure in Molecular Biology. The Smith-Waterman algorithm has been available for more than 25 years. It is based on a dynamic programming approach that explores all the possible alignments between two sequences; as a result it returns the optimal local alignment. Unfortunately, the computational cost is very high, requiring a number of operations proportional to the product of the length of two sequences. Furthermore, the exponential growth of protein and DNA databases makes the Smith-Waterman algorithm unrealistic for searching similarities in large sets of sequences. For these reasons heuristic approaches such as those implemented in FASTA and BLAST tend to be preferred, allowing faster execution times at the cost of reduced sensitivity. The main motivation of our work is to exploit the huge computational power of commonly available graphic cards, to develop high performance solutions for sequence alignment. Results In this paper we present what we believe is the fastest solution of the exact Smith-Waterman algorithm running on commodity hardware. It is implemented in the recently released CUDA programming environment by NVidia. CUDA allows direct access to the hardware primitives of the last-generation Graphics Processing Units (GPU) G80. Speeds of more than 3.5 GCUPS (Giga Cell Updates Per Second) are achieved on a workstation running two GeForce 8800 GTX. Exhaustive tests have been done to compare our implementation to SSEARCH and BLAST, running on a 3 GHz Intel Pentium IV processor. Our solution was also compared to a recently published GPU implementation and to a Single Instruction Multiple Data (SIMD) solution. These tests show that our implementation performs from 2 to 30 times faster than any other previous attempt available on commodity hardware. Conclusions The results show that graphic cards are now sufficiently advanced to be used as efficient hardware accelerators for sequence alignment. Their performance is better than any alternative available on commodity hardware platforms. The solution presented in this paper allows large scale alignments to be performed at low cost, using the exact Smith-Waterman algorithm instead of the largely adopted heuristic approaches