Bridging the Gap between Genotype and Phenotype via Network Approaches

In the last few years we have witnessed tremendous progress in detecting associations between genetic variations and complex traits. While genome-wide association studies have been able to discover genomic regions that may influence many common human diseases, these discoveries created an urgent need for methods that extend the knowledge of genotype-phenotype relationships to the level of the molecular mechanisms behind them. To address this emerging need, computational approaches increasingly utilize a pathway-centric perspective. These new methods often utilize known or predicted interactions between genes and/or gene products. In this review, we survey recently developed network based methods that attempt to bridge the genotype-phenotype gap. We note that although these methods help narrow the gap between genotype and phenotype relationships, these approaches alone cannot provide the precise details of underlying mechanisms and current research is still far from closing the gap

Algorithms for Data Migration

This thesis is concerned with the problem related to data storage and management. A large storage server consists of several hundreds of disks. To balance the load across disks, the system computes data layouts that are typically adjusted according to the workload. As workloads change over time, the system recomputes the data layout, and rearranges the data items according to the new layout. We identify the problem of computing an efficient data migration plan that converts an initial layout to a target layout. We define the data migration problem as follows: for each item, there are a set of disks that have the item (sources) and a set of disks that want to receive the item (destinations). We want to migrate the data items from the sources to destinations. The crucial constraint is that each disk can participate in only one transfer at a time. The most common objective has been to minimize the makespan, which is the time when we finish all the migrations. The problem is NP-hard, and we develop polynomial time algorithms with constant factor approximation guarantees and several other heuristic algorithms. We present the performance evaluation of the different methods through an experimental study. We also consider the data migration problem to minimize the sum of completion times over all migration jobs or storage devices. Minimizing the sum of completion times of jobs is one of the most common objectives in scheduling literature. On the other hand, since a storage device may run inefficiently while the device is involved in migrations, another interesting objective is to minimize the sum of completion times over all storage devices. We present hardness results and constant factor approximation algorithms for these objectives. In addition, we consider the case when we have a heterogeneous collection of machines. We assume that heterogeneity is modeled by a non-uniform speed of the sending machine. For the basic problem of multicasting and broadcasting in the model, we show that Fastest Node First scheme gives a approximation ratio of 1.5 for minimizing the makespan. We also prove that there is a polynomial time approximation scheme

BeWith: A Between-Within Method to Discover Relationships between Cancer Modules via Integrated Analysis of Mutual Exclusivity, Co-occurrence and Functional Interactions

The analysis of the mutational landscape of cancer, including mutual exclusivity and co-occurrence of mutations, has been instrumental in studying the disease. We hypothesized that exploring the interplay between co-occurrence, mutual exclusivity, and functional interactions between genes will further improve our understanding of the disease and help to uncover new relations between cancer driving genes and pathways. To this end, we designed a general framework, BeWith, for identifying modules with different combinations of mutation and interaction patterns. We focused on three different settings of the BeWith schema: (i) BeME-WithFun in which the relations between modules are enriched with mutual exclusivity while genes within each module are functionally related; (ii) BeME-WithCo which combines mutual exclusivity between modules with co-occurrence within modules; and (iii) BeCo-WithMEFun which ensures co-occurrence between modules while the within module relations combine mutual exclusivity and functional interactions. We formulated the BeWith framework using Integer Linear Programming (ILP), enabling us to find optimally scoring sets of modules. Our results demonstrate the utility of BeWith in providing novel information about mutational patterns, driver genes, and pathways. In particular, BeME-WithFun helped identify functionally coherent modules that might be relevant for cancer progression. In addition to finding previously well-known drivers, the identified modules pointed to the importance of the interaction between NCOR and NCOA3 in breast cancer. Additionally, an application of the BeME-WithCo setting revealed that gene groups differ with respect to their vulnerability to different mutagenic processes, and helped us to uncover pairs of genes with potentially synergetic effects, including a potential synergy between mutations in TP53 and metastasis related DCC gene

Proinflammatory Role of Vascular Endothelial Growth Factor in the Pathogenesis of Rheumatoid Arthritis: Prospects for Therapeutic Intervention

Recent experimental and clinical studies have placed new emphasis on the role of angiogenesis in chronic inflammatory disease. Vascular endothelial growth factor (VEGF) and its receptors are the best characterized system in the regulation of rheumatoid arthritis (RA) by angiogenesis. Furthermore, in addition to its angiogenic role, VEGF can act as a direct proinflammatory mediator during the pathogenesis of RA, and protect rheumatoid synoviocytes from apoptosis, which contributes to synovial hyperplasia. Therefore, the developments of synovial inflammation, hyperplasia, and angiogenesis in the joints of RA patients seem to be regulated by a common cue, namely, VEGF. Agents that target VEGF, such as anti-VEGF antibody and aptamer, have yielded promising clinical data in patients with cancer or macular degeneration, and in RA patients, pharmacologic modulations targeting VEGF or its receptor may offer new therapeutic approaches. In this review, the authors integrate current knowledge of VEGF signaling and information on VEGF antagonists gleaned experimentally and place emphasis on the use of synthetic anti-VEGF hexapeptide to prevent VEGF interacting with its receptor

Effects of Single Nucleotide Polymorphism Marker Density on Haplotype Block Partition

Many researchers have found that one of the most important characteristics of the structure of linkage disequilibrium is that the human genome can be divided into non-overlapping block partitions in which only a small number of haplotypes are observed. The location and distribution of haplotype blocks can be seen as a population property influenced by population genetic events such as selection, mutation, recombination and population structure. In this study, we investigate the effects of the density of markers relative to the full set of all polymorphisms in the region on the results of haplotype partitioning for five popular haplotype block partition methods: three methods in Haploview (confidence interval, four gamete test, and solid spine), MIG++ implemented in PLINK 1.9 and S-MIG++. We used several experimental datasets obtained by sampling subsets of single nucleotide polymorphism (SNP) markers of chromosome 22 region in the 1000 Genomes Project data and also the HapMap phase 3 data to compare the results of haplotype block partitions by five methods. With decreasing sampling ratio down to 20% of the original SNP markers, the total number of haplotype blocks decreases and the length of haplotype blocks increases for all algorithms. When we examined the marker-independence of the haplotype block locations constructed from the datasets of different density, the results using below 50% of the entire SNP markers were very different from the results using the entire SNP markers. We conclude that the haplotype block construction results should be used and interpreted carefully depending on the selection of markers and the purpose of the study

Channel Assignment for Multiple Interface Nodes in Wireless Ad

In wireless networks, due to the broadcast property of the medium, nodes close to each other cannot simultaneously transmit over the same channel. One way to overcome this limitation is to use multiple independent channels available in the system. Although we can use a single wireless interface card to access multiple channels, such schemes can raise issues of compatibility (e.g., modication of the MAC protocol) and performance degradation (e.g., due to frequent channel switching). In this paper, we assume that nodes are equipped with multiple interface cards, and focus on the channel assignment problem for minimizing the total number of interferences among wireless links. We show that the problem is NP-hard and present distributed heuristics. We also present two centralized algorithms and show that the algorithms give constant factor approximation guarantees. We perform simulation experiments for the proposed distributed heuristic. The results show that compared to one-channel scenarios, our proposed algorithm can reduce the number of interferences by up to 85% when nodes are equipped with four interface cards. Through detailed packetlevel simulation experiments, we also show that depending on the scenario, the resulting channel assignment actually achieves up to seven times throughput improvement over the single-channel case

Wheel Impact Test by Deep Learning: Prediction of Location and Magnitude of Maximum Stress

The impact performance of the wheel during wheel development must be ensured through a wheel impact test for vehicle safety. However, manufacturing and testing a real wheel take a significant amount of time and money because developing an optimal wheel design requires numerous iterative processes of modifying the wheel design and verifying the safety performance. Accordingly, the actual wheel impact test has been replaced by computer simulations, such as Finite Element Analysis (FEA), but it still requires high computational costs for modeling and analysis. Moreover, FEA experts are needed. This study presents an aluminum road wheel impact performance prediction model based on deep learning that replaces the computationally expensive and time-consuming 3D FEA. For this purpose, 2D disk-view wheel image data, 3D wheel voxel data, and barrier mass value used for wheel impact test are utilized as the inputs to predict the magnitude of maximum von Mises stress, corresponding location, and the stress distribution of 2D disk-view. The wheel impact performance prediction model can replace the impact test in the early wheel development stage by predicting the impact performance in real time and can be used without domain knowledge. The time required for the wheel development process can be shortened through this mechanism

MicroRNA-143 and-145 modulate the phenotype of synovial fibroblasts in rheumatoid arthritis

Fibroblast-like synoviocytes (FLSs) constitute a major cell subset of rheumatoid arthritis (RA) synovia. Dysregulation of microRNAs (miRNAs) has been implicated in activation and proliferation of RA-FLSs. However, the functional association of various miRNAs with their targets that are characteristic of the RA-FLS phenotype has not been globally elucidated. In this study, we performed microarray analyses of miRNAs and mRNAs in RA-FLSs and osteoarthritis FLSs (OA-FLSs), simultaneously, to validate how dysregulated miRNAs may be associated with the RA-FLS phenotype. Global miRNA profiling revealed that miR-143 and miR-145 were differentially upregulated in RA-FLSs compared to OA-FLSs. miR-143 and miR-145 were highly expressed in independent RA-FLSs. The miRNA-target prediction and network model of the predicted targets identified insulin-like growth factor binding protein 5 (IGFBP5) and semaphorin 3A (SEMA3A) as potential target genes downregulated by miR-143 and miR-145, respectively. IGFBP5 level was inversely correlated with miR-143 expression, and its deficiency rendered RA-FLSs more sensitive to TNFα stimulation, promoting IL-6 production and NF-κB activity. Moreover, SEMA3A was a direct target of miR-145, as determined by a luciferase reporter assay, antagonizing VEGF165-induced increases in the survival, migration and invasion of RA-FLSs. Taken together, our data suggest that enhanced expression of miR-143 and miR-145 renders RA-FLSs susceptible to TNFα and VEGF165 stimuli by downregulating IGFBP5 and SEMA3A, respectively, and that these miRNAs could be therapeutic targets. © 2017 KSBMB4

MLN51 and GM-CSF involvement in the proliferation of fibroblast-like synoviocytes in the pathogenesis of rheumatoid arthritis

Rheumatoid arthritis (RA) is an inflammatory autoimmune disease of unclear etiology. This study was conducted to identify critical factors involved in the synovial hyperplasia in RA pathology. We applied cDNA microarray analysis to profile the gene expressions of RA fibroblast-like synoviocytes (FLSs) from patients with RA. We found that the MLN51 (metastatic lymph node 51) gene, identified in breast cancer, is remarkably upregulated in the hyperactive RA FLSs. However, growth-retarded RA FLSs passaged in vitro expressed small quantities of MLN51. MLN51 expression was significantly enhanced in the FLSs when the growth-retarded FLSs were treated with granulocyte – macrophage colony-stimulating factor (GM-CSF) or synovial fluid (SF). Anti-GM-CSF neutralizing antibody blocked the MLN51 expression even though the FLSs were cultured in the presence of SF. In contrast, GM-CSF in SFs existed at a significant level in the patients with RA (n = 6), in comparison with the other inflammatory cytokines, IL-1β and TNF-α. Most RA FLSs at passage 10 or more recovered from their growth retardation when cultured in the presence of SF. The SF-mediated growth recovery was markedly impaired by anti-GM-CSF antibody. Growth-retarded RA FLSs recovered their proliferative capacity after treatment with GM-CSF in a dose-dependent manner. However, MLN51 knock-down by siRNA completely blocked the GM-CSF/SF-mediated proliferation of RA FLSs. Taken together, our results imply that MLN51, induced by GM-CSF, is important in the proliferation of RA FLSs in the pathogenesis of RA