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Background: It is unclear whether increased left ventricular (LV) thickness is associated with worse clinical outcomes in severe aortic stenosis (AS). The aim of this study was to determine the effect of increased LV wall thickness (LVWT) on major clinical outcomes in patients with severe AS., Methods and Results: This study included 290 severe AS patients (mean age 69.4 ± 11.0 years; 136 females) between January 2008 and December 2018. For outcome assessment, the endpoint was defined as death from all causes, cardiovascular death, and the aortic valve replacement (AVR) surgery rate. During follow-up (48.7 ± 39.0 months), 157 patients had AVR, 43 patients died, and 28 patients died from cardiovascular causes. Patients with increased LVWT underwent AVR surgery much more than those without LVWT (60.0% vs. 39.0%, p < 0.001). Furthermore, in patients with increased LVWT, the all-cause and cardiovascular death rates were significantly lower in the AVR group than in the non-AVR group (8.8% vs. 27.3%, p < 0.001, 4.8%, vs. 21.0%, p < 0.001). Multivariate analysis revealed that increased LVWT, age, dyspnea, and AVR surgery were significantly correlated with cardiovascular death., Conclusions: In patients with severe AS, increased LVWT was associated with a higher AVR surgery rate and an increased rate of cardiovascular death independent of other well-known prognostic variates. Thus, these findings suggest that increased LVWT might be used as a potential prognostic factor in severe AS patients.

Background: The term triple-negative breast cancer (TNBC) is used to describe breast cancers without expression of estrogen receptor, progesterone receptor or HER2 amplification. To advance targeted treatment options for TNBC, it is critical that the subtypes within this classification be described in regard to their characteristic biology and gene expression. The Cancer Genome Atlas (TCGA) dataset provides not only clinical and mRNA expression data but also expression data for microRNAs., Results: In this study, we applied the Lehmann classifier to TCGA-derived TNBC cases which also contained microRNA expression data and derived subtype-specific microRNA expression patterns. Subsequent analyses integrated known and predicted microRNA-mRNA regulatory nodes as well as patient survival data to identify key networks. Notably, basal-like 1 (BL1) TNBCs were distinguished from basal-like 2 TNBCs through up-regulation of members of the miR-17-92 cluster of microRNAs and suppression of several known miR-17-92 targets including inositol polyphosphate 4-phosphatase type II, INPP4B., Conclusions: These data demonstrate TNBC subtype-specific microRNA and target mRNA expression which may be applied to future biomarker and therapeutic development studies.

Background: Atherosclerotic cardiovascular (CV) events commonly occur in individuals with a low CV risk burden. This study evaluated the ability of the triglyceride glucose (TyG) index to predict subclinical coronary artery disease (CAD) in asymptomatic subjects without traditional CV risk factors (CVRFs)., Methods: This retrospective, cross-sectional, and observational study evaluated the association of TyG index with CAD in 1250 (52.8 ± 6.5 years, 46.9% male) asymptomatic individuals without traditional CVRFs (defined as systolic/diastolic blood pressure ≥ 140/90 mmHg; fasting glucose ≥126 mg/dL; total cholesterol ≥240 mg/dL; low-density lipoprotein cholesterol ≥160 mg/dL; high-density lipoprotein cholesterol < 40 mg/dL; body mass index ≥25.0 kg/m 2 ; current smoking; and previous medical history of hypertension, diabetes, or dyslipidemia). CAD was defined as the presence of any coronary plaque on coronary computed tomographic angiography. The participants were divided into three groups based on TyG index tertiles., Results: The prevalence of CAD increased with elevating TyG index tertiles (group I: 14.8% vs. group II: 19.3% vs. group III: 27.6%; P < 0.001). Multivariate logistic regression models showed that TyG index was associated with an increased risk of CAD (odds ratio [OR] 1.473, 95% confidence interval [CI] 1.026-2.166); especially non-calcified (OR 1.581, 95% CI 1.002-2.493) and mixed plaques (OR 2.419, 95% CI 1.051-5.569) (all P < 0.05). The optimal TyG index cut-off for predicting CAD was 8.44 (sensitivity 47.9%; specificity 68.5%; area under the curve 0.600; P < 0.001). The predictive value of this cut-off improved after considering the non-modifiable factors of old age and male sex., Conclusions: TyG index is an independent marker for predicting subclinical CAD in individuals conventionally considered healthy.

Background: Cross-species analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved interaction patterns. Identifying such conserved substructures between PPI networks of different species increases our understanding of the principles deriving evolution of cellular organizations and their functions in a system level. In recent years, network alignment techniques have been applied to genome-scale PPI networks to predict evolutionary conserved modules. Although a wide variety of network alignment algorithms have been introduced, developing a scalable local network alignment algorithm with high accuracy is still challenging., Results: We present a novel pairwise local network alignment algorithm, called LePrimAlign, to predict conserved modules between PPI networks of three different species. The proposed algorithm exploits the results of a pairwise global alignment algorithm with many-to-many node mapping. It also applies the concept of graph entropy to detect initial cluster pairs from two networks. Finally, the initial clusters are expanded to increase the local alignment score that is formulated by a combination of intra-network and inter-network scores. The performance comparison with state-of-the-art approaches demonstrates that the proposed algorithm outperforms in terms of accuracy of identified protein complexes and quality of alignments., Conclusion: The proposed method produces local network alignment of higher accuracy in predicting conserved modules even with large biological networks at a reduced computational cost.

Background: Aortic dilatation is a major risk factor for aortic dissection. The aim of the present study was to assess the relationship between left ventricular (LV) geometry and maximal ascending aorta (MAA)., Methods: We reviewed data from patients who were diagnosed with acute type A aortic dissection and who underwent surgical management from December 2002 to March 2016 at Dong-A University Hospital. Among 151 patients with non-Marfan aortic dissection in the study, 50 who had echocardiography preoperatively were investigated and MAA diameter was analyzed by LV geometric patterns., Results: Patients' mean age was 59.6 ± 13.5 years and 38.0% were male. The mean MAA diameter was 52.9 ± 8.5 mm. MAA diameter was significantly correlated with LV mass index (r = 0.62, P < 0.001). On analysis by LV geometry, MAA diameter showed a significant difference between the 4 groups (P = 0.02), and the eccentric and concentric hypertrophy groups showed significantly larger MAA diameter than the other two groups., Conclusion: MAA diameter was associated with LV mass index and was significantly different between LV geometry types. In this study, not only concentric hypertrophy but also eccentric LV hypertrophy was related to larger MAA in type A aortic dissection patients.

Background: Protein-protein interactions (PPIs) play a key role in understanding the mechanisms of cellular processes. The availability of interactome data has catalyzed the development of computational approaches to elucidate functional behaviors of proteins on a system level. Gene Ontology (GO) and its annotations are a significant resource for functional characterization of proteins. Because of wide coverage, GO data have often been adopted as a benchmark for protein function prediction on the genomic scale., Results: We propose a computational approach, called M-Finder, for functional association pattern mining. This method employs semantic analytics to integrate the genome-wide PPIs with GO data. We also introduce an interactive web application tool that visualizes a functional association network linked to a protein specified by a user. The proposed approach comprises two major components. First, the PPIs that have been generated by high-throughput methods are weighted in terms of their functional consistency using GO and its annotations. We assess two advanced semantic similarity metrics which quantify the functional association level of each interacting protein pair. We demonstrate that these measures outperform the other existing methods by evaluating their agreement to other biological features, such as sequence similarity, the presence of common Pfam domains, and core PPIs. Second, the information flow-based algorithm is employed to discover a set of proteins functionally associated with the protein in a query and their links efficiently. This algorithm reconstructs a functional association network of the query protein. The output network size can be flexibly determined by parameters., Conclusions: M-Finder provides a useful framework to investigate functional association patterns with any protein. This software will also allow users to perform further systematic analysis of a set of proteins for any specific function. It is available online at http://bionet.ecs.baylor.edu/mfinder.

Background: In stark contrast to the wealth of detail about C. elegans developmental biology and molecular genetics, biologists lack basic data for understanding the abundance and distribution of Caenorhabditis species in natural areas that are unperturbed by human influence., Methods: Here we report the analysis of dense sampling from a small, remote site in the Amazonian rain forest of the Nouragues Natural Reserve in French Guiana., Results: Sampling of rotting fruits and flowers revealed proliferating populations of Caenorhabditis, with up to three different species co-occurring within a single substrate sample, indicating remarkable overlap of local microhabitats. We isolated six species, representing the highest local species richness for Caenorhabditis encountered to date, including both tropically cosmopolitan and geographically restricted species not previously isolated elsewhere. We also documented the structure of within-species molecular diversity at multiple spatial scales, focusing on 57 C. briggsae isolates from French Guiana. Two distinct genetic subgroups co-occur even within a single fruit. However, the structure of C. briggsae population genetic diversity in French Guiana does not result from strong local patterning but instead presents a microcosm of global patterns of differentiation. We further integrate our observations with new data from nearly 50 additional recently collected C. briggsae isolates from both tropical and temperate regions of the world to re-evaluate local and global patterns of intraspecific diversity, providing the most comprehensive analysis to date for C. briggsae population structure across multiple spatial scales., Conclusions: The abundance and species richness of Caenorhabditis nematodes is high in a Neotropical rainforest habitat that is subject to minimal human interference. Microhabitat preferences overlap for different local species, although global distributions include both cosmopolitan and geographically restricted groups. Local samples for the cosmopolitan C. briggsae mirror its pan-tropical patterns of intraspecific polymorphism. It remains an important challenge to decipher what drives Caenorhabditis distributions and diversity within and between species.

Background: Recent computational techniques have facilitated analyzing genome-wide protein-protein interaction data for several model organisms. Various graph-clustering algorithms have been applied to protein interaction networks on the genomic scale for predicting the entire set of potential protein complexes. In particular, the density-based clustering algorithms which are able to generate overlapping clusters, i.e. the clusters sharing a set of nodes, are well-suited to protein complex detection because each protein could be a member of multiple complexes. However, their accuracy is still limited because of complex overlap patterns of their output clusters., Results: We present a systematic approach of refining the overlapping clusters identified from protein interaction networks. We have designed novel metrics to assess cluster overlaps: overlap coverage and overlapping consistency. We then propose an overlap refinement algorithm. It takes as input the clusters produced by existing density-based graph-clustering methods and generates a set of refined clusters by parameterizing the metrics. To evaluate protein complex prediction accuracy, we used the f-measure by comparing each refined cluster to known protein complexes. The experimental results with the yeast protein-protein interaction data sets from BioGRID and DIP demonstrate that accuracy on protein complex prediction has increased significantly after refining cluster overlaps., Conclusions: The effectiveness of the proposed cluster overlap refinement approach for protein complex detection has been validated in this study. Analyzing overlaps of the clusters from protein interaction networks is a crucial task for understanding of functional roles of proteins and topological characteristics of the functional systems.

Background: The prophenoloxidase-activating (PO activating) system plays an important role in the crustacean innate immunity, particularly in wound healing and pathogen defense. A key member of this system is prophenoloxidase-activating enzyme (PPAE), which is the direct activator of prophenoloxidase (proPO). Despite their importance in crustacean PO activating system, the studies on them remain limited., Results: Here we report on a PPAE of white shrimp, Litopenaeus vannamei (lvPPAE1), which showed 94% similarity to PPAE1 of Penaeus monodon. We found that lvPPAE1 in fluid hemocytes was down regulated after challenge by Vibrio harveyi but was enhanced when shrimps were exposed to a bacteria-rich environment for long-term. In vivo gene silence of lvPPAE1 by RNAi can significantly reduce the phenoloxidase activity (PO) and increase the susceptibility of shrimps to V. harveyi. Although lvPPAE1 was down-regulated in fluid hemocytes by Vibrio challenge, its expression increased significantly in gill after bacteria injection, which is the primary bacteria-clearance tissue., Conclusion: Suppressed expression in fluid hemocytes and enhanced expression in gill indicates selectively enhanced expression at the bacterial clearance site. This is a novel feature for PPAE expression. The results will contribute to our understanding of the PO activating system in crustaceans.

Background: Angiotensin-converting enzyme inhibitors and the angiotensin-receptor blocker valsartan ameliorate ventricular remodeling after myocardial infarction (MI). Based on previous clinical trials, a maximum clinical dose is recommended in practical guidelines. Yet, has not been clearly demonstrated whether the recommended dose is more efficacious compared to the lower dose that is commonly used in clinical practice., Method/design: Valsartan in post-MI remodeling (VALID) is a randomized, open-label, single-blinded multicenter study designed to compare the efficacy of different clinical dose of valsartan on the post-MI ventricular remodeling. This study also aims to assess neurohormone change and clinical parameters of patients during the post-infarct period. A total of 1116 patients with left ventricular dysfunction following the first episode of acute ST-elevation MI are to be enrolled and randomized to a maximal tolerable dose (up to 320 mg/day) or usual dose (80 mg/day) of valsartan for 12 months in 2:1 ratio. Echocardiographic analysis for quantifying post-MI ventricular remodeling is to be conducted in central core laboratory. Clinical assessment and laboratory test are performed at fixed times., Discussion: VALID is a multicenter collaborative study to evaluate the impact of dose of valsartan on the post-MI ventricular remodeling. The results of the study provide information about optimal dosing of the drug in the management of patients after MI. The results will be available by 2012., Trial Registration: NCT01340326.

Background: Protein-protein interactions play a key role in biological processes of proteins within a cell. Recent high-throughput techniques have generated protein-protein interaction data in a genome-scale. A wide range of computational approaches have been applied to interactome network analysis for uncovering functional organizations and pathways. However, they have been challenged because of complex connectivity. It has been investigated that protein interaction networks are typically characterized by intrinsic topological features: high modularity and hub-oriented structure. Elucidating the structural roles of modules and hubs is a critical step in complex interactome network analysis., Results: We propose a novel approach to convert the complex structure of an interactome network into hierarchical ordering of proteins. This algorithm measures functional similarity between proteins based on the path strength model, and reveals a hub-oriented tree structure hidden in the complex network. We score hub confidence and identify functional modules in the tree structure of proteins, retrieved by our algorithm. Our experimental results in the yeast protein interactome network demonstrate that the selected hubs are essential proteins for performing functions. In network topology, they have a role in bridging different functional modules. Furthermore, our approach has high accuracy in identifying functional modules hierarchically distributed., Conclusions: Decomposing, converting, and synthesizing complex interaction networks are fundamental tasks for modeling their structural behaviors. In this study, we systematically analyzed complex interactome network structures for retrieving functional information. Unlike previous hierarchical clustering methods, this approach dynamically explores the hierarchical structure of proteins in a global view. It is well-applicable to the interactome networks in high-level organisms because of its efficiency and scalability.

Background: The functional characterization of newly discovered proteins has been a challenge in the post-genomic era. Protein-protein interactions provide insights into the functional analysis because the function of unknown proteins can be postulated on the basis of their interaction evidence with known proteins. The protein-protein interaction data sets have been enriched by high-throughput experimental methods. However, the functional analysis using the interaction data has a limitation in accuracy because of the presence of the false positive data experimentally generated and the interactions that are a lack of functional linkage., Results: Protein-protein interaction data can be integrated with the functional knowledge existing in the Gene Ontology (GO) database. We apply similarity measures to assess the functional similarity between interacting proteins. We present a probabilistic framework for predicting functions of unknown proteins based on the functional similarity. We use the leave-one-out cross validation to compare the performance. The experimental results demonstrate that our algorithm performs better than other competing methods in terms of prediction accuracy. In particular, it handles the high false positive rates of current interaction data well., Conclusion: The experimentally determined protein-protein interactions are erroneous to uncover the functional associations among proteins. The performance of function prediction for uncharacterized proteins can be enhanced by the integration of multiple data sources available.

Background: Quantitative characterization of the topological characteristics of protein-protein interaction (PPI) networks can enable the elucidation of biological functional modules. Here, we present a novel clustering methodology for PPI networks wherein the biological and topological influence of each protein on other proteins is modeled using the probability distribution that the series of interactions necessary to link a pair of distant proteins in the network occur within a time constant (the occurrence probability)., Results: CASCADE selects representative nodes for each cluster and iteratively refines clusters based on a combination of the occurrence probability and graph topology between every protein pair. The CASCADE approach is compared to nine competing approaches. The clusters obtained by each technique are compared for enrichment of biological function. CASCADE generates larger clusters and the clusters identified have p-values for biological function that are approximately 1000-fold better than the other methods on the yeast PPI network dataset. An important strength of CASCADE is that the percentage of proteins that are discarded to create clusters is much lower than the other approaches which have an average discard rate of 45% on the yeast protein-protein interaction network., Conclusion: CASCADE is effective at detecting biologically relevant clusters of interactions.

Background: The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms., Results: We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches., Conclusion: The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.

Background: The sparse connectivity of protein-protein interaction data sets makes identification of functional modules challenging. The purpose of this study is to critically evaluate a novel clustering technique for clustering and detecting functional modules in protein-protein interaction networks, termed STM., Results: STM selects representative proteins for each cluster and iteratively refines clusters based on a combination of the signal transduced and graph topology. STM is found to be effective at detecting clusters with a diverse range of interaction structures that are significant on measures of biological relevance. The STM approach is compared to six competing approaches including the maximum clique, quasi-clique, minimum cut, betweeness cut and Markov Clustering (MCL) algorithms. The clusters obtained by each technique are compared for enrichment of biological function. STM generates larger clusters and the clusters identified have p-values that are approximately 125-fold better than the other methods on biological function. An important strength of STM is that the percentage of proteins that are discarded to create clusters is much lower than the other approaches., Conclusion: STM outperforms competing approaches and is capable of effectively detecting both densely and sparsely connected, biologically relevant functional modules with fewer discards.