Intramyocardial Injections to De-Stiffen the Heart: A Subject-Specific in Silico Approach

We hypothesized that minimally invasive injections of a softening agent at strategic locations in stiff myocardium could de-stiffen the left ventricle (LV) globally. Physics-based finite element models of the LV were created from LV echocardiography images and pressures recorded during experiments in four swine. Results confirmed animal models of LV softening by systemic agents. Regional de-stiffening of myocardium led to global de-stiffening of LV. The mathematical set up was used to design LV global de-stiffening by regional softening of myocardium. At an end diastolic pressure of 23 mmHg, when 8 ml of the free wall was covered by intramyocardial injections, end diastolic volume (EDV) increased by 15.0%, whereas an increase up to 11 ml due to intramyocardial injections in the septum and free wall led to a 26.0% increase in EDV. Although the endocardial intramyocardial injections occupied a lower LV wall volume, they led to an EDV (44 ml) that was equal compared to intramyocardial injections in the mid-wall (44 ml) and larger compared to intramyocardial injections in the epicardium (41 ml). Using an in silico set up, sites of regional myocardium de-stiffening could be planned in order to globally soften overly stiff LV in heart failure with preserved ejection fraction. This novel treatment is built on subject-specific data. Hypothesis-testing of these simulation findings in animal models is warranted.     

k-gram方法识别microRNA前体

MicroRNAs(miRNAs)是动植物中较短的参与调控基因表达的功能性非编码RNA序列.第一个miRNA是通过实验手段发现的,然而通过实验手段识别miRNA在技术上仍然具有很大的挑战性和不完整性.因此,miRNA基因识别需要寻求计算方法来弥补实验方法的不足.提出了一个全新的miRNA前体的识别方法.在构造识别模型中,把初级序列和序列二级结构相结合,采用k-gram方法把序列信息映射到高维特征空间中,然后通过特征选取方法提取特征,并用这些特征为miRNA前体的识别构造了基于SVM的识别模型.同时,采用隐马尔可夫模型(HMM)的学习方法进行了比较.实验结果表明,该方法是有效的,可以达到较高的敏感性和特异性.     

TMT Labeling for the Masses: A Robust and Cost-efficient,In-solution Labeling Approach

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Highlights

• •TMT labeling protocol with excellent intra- and interlaboratory reproducibility.
• •Complete in-solution labeling of peptides using 1/8 of recommended TMT quantities.
• •Demonstration of utility for deep-scale (phospho)proteome analysis.

     

An Approach for Identifying Brainstem Dopaminergic Pathways Using Resting State Functional MRI

Here, we present an approach for identifying brainstem dopaminergic pathways using resting state functional MRI. In a group of healthy individuals, we searched for significant functional connectivity between dopamine-rich midbrain areas (substantia nigra; ventral tegmental area) and a striatal region (caudate) that was modulated by both a pharmacological challenge (the administration of the dopaminergic agonist bromocriptine) and a dopamine-sensitive cognitive trait (an individual’s working memory capacity). A significant inverted-U shaped connectivity pattern was found in a subset of midbrain-striatal connections, demonstrating that resting state fMRI data is sufficiently powerful to identify brainstem neuromodulatory brain networks.     

Functional Regions of Interest in Electrical Impedance Tomography: A Secondary Analysis of Two Clinical Studies

Introduction

Patients with acute respiratory distress syndrome (ARDS) typically show a high degree of ventilation inhomogeneity, which is associated with morbidity and unfavorable outcomes. Electrical impedance tomography (EIT) is able to detect ventilation inhomogeneity, but it is unclear which method for defining the region of interest (ROI) should be used for this purpose. The aim of our study was to compare the functional region of interest (fROI) method to both the lung area estimation method (LAEM) and no ROI when analysing global parameters of ventilation inhomogeneity. We assumed that a good method for ROI determination would lead to a high discriminatory power for ventilation inhomogeneity, as defined by the area under the receiver operating characteristics curve (AUC), comparing patients suffering from ARDS and control patients without pulmonary pathologies.

Methods

We retrospectively analysed EIT data from 24 ARDS patients and 12 control patients without pulmonary pathology. In all patients, a standardized low-flow-pressure volume maneuver had been performed and was used for EIT image generation. We compared the AUC for global inhomogeneity (GI) index and coefficient of variation (CV) between ARDS and control patients using all EIT image pixels, the fROI method and the LAEM for ROI determination.

Results

When analysing all EIT image pixels, we found an acceptable AUC both for the GI index (AUC = 0.76; 95% confidence interval (CI) 0.58–0.94) and the CV (AUC = 0.74; 95% CI 0.55–0.92). With the fROI method, we found a deteriorating AUC with increasing threshold criteria. With the LAEM, we found the best AUC both for the GI index (AUC = 0.89; 95% CI 0.78–1.0) and the CV (AUC = 0.89; 95% CI 0.78–1.0) using a threshold criterion of 50% of the maximum tidal impedance change.

Conclusion

In the assessment of ventilation inhomogeneity with EIT, functional regions of interest obscure the difference between patients with ARDS and control patients without pulmonary pathologies. The LAEM is preferable to the fROI method when assessing ventilation inhomogeneity.     

Functional Genomics Approach to Identifying Genes Required for Biofilm Development by Streptococcus mutans

Streptococcus mutans, the primary etiological agent of human dental caries, is an obligate biofilm-forming bacterium. The goals of this study were to identify the gene(s) required for biofilm formation by this organism and to elucidate the role(s) that some of the known global regulators of gene expression play in controlling biofilm formation. In S. mutans UA159, the brpA gene (for biofilm regulatory protein) was found to encode a novel protein of 406 amino acid residues. A strain carrying an insertionally inactivated copy of brpA formed longer chains than did the parental strain, aggregated in liquid culture, and was unable to form biofilms as shown by an in vitro biofilm assay. A putative homologue of the enzyme responsible for synthesis of autoinducer II (AI-2) of the bacterial quorum-sensing system was also identified in S. mutans UA159, but insertional inactivation of the gene (luxS_Sm) did not alter colony or cell morphology or diminish the capacity of S. mutans to form biofilms. We also examined the role of the homologue of the Bacillus subtilis catabolite control protein CcpA in S. mutans in biofilm formation, and the results showed that loss of CcpA resulted in about a 60% decrease in the ability to form biofilms on an abiotic surface. From these data, we conclude that CcpA and BrpA may regulate genes that are required for stable biofilm formation by S. mutans.     

k-gram方法识别microRNA前体

MicroRNAs (miRNAs) 是动植物中较短的参与调控基因表达的功能性非编码RNA序列. 第一个miRNA是通过实验手段发现的，然而通过实验手段识别miRNA在技术上仍然具有很大的挑战性和不完整性. 因此，miRNA基因识别需要寻求计算方法来弥补实验方法的不足. 提出了一个全新的miRNA前体的识别方法. 在构造识别模型中，把初级序列和序列二级结构相结合，采用k-gram方法把序列信息映射到高维特征空间中，然后通过特征选取方法提取特征，并用这些特征为miRNA前体的识别构造了基于SVM的识别模型. 同时，采用隐马尔可夫模型(HMM)的学习方法进行了比较. 实验结果表明，该方法是有效的，可以达到较高的敏感性和特异性.     

A Modified Entropy-Based Approach for Identifying Gene-Gene Interactions in Case-Control Study

Gene-gene interactions may play an important role in the genetics of a complex disease. Detection and characterization of gene-gene interactions is a challenging issue that has stimulated the development of various statistical methods to address it. In this study, we introduce a method to measure gene interactions using entropy-based statistics from a contingency table of trait and genotype combinations. We also developed an exploration procedure by using graphs. We propose a standardized relative information gain (RIG) measure to evaluate the interactions between single nucleotide polymorphism (SNP) combinations. To identify the k ^th order interactions, contingency tables of trait and genotype combinations of k SNPs are constructed, with which RIGs are calculated. The RIGs are standardized using the mean and standard deviation from the permuted datasets. SNP combinations yielding high standardized RIG are chosen for gene-gene interactions. Detection of high-order interactions and comparison of interaction strengths between different orders are made possible by using standardized RIG. We have applied the proposed standardized entropy-based method to two types of data sets from a simulation study and a real genetic association study. We have compared our method and the multifactor dimensionality reduction (MDR) method through power analysis of eight different genetic models with varying penetrance rates, number of SNPs, and sample sizes. Our method shows successful identification of genetic associations and gene-gene interactions both in simulation and real genetic data. Simulation results suggest that the proposed entropy-based method is better able to detect high-order interactions and is superior to the MDR method in most cases. The proposed method is well suited for detecting interactions without main effects as well as for models including main effects.     

A Model-Based Approach for Identifying Signatures of Ancient Balancing Selection in Genetic Data

While much effort has focused on detecting positive and negative directional selection in the human genome, relatively little work has been devoted to balancing selection. This lack of attention is likely due to the paucity of sophisticated methods for identifying sites under balancing selection. Here we develop two composite likelihood ratio tests for detecting balancing selection. Using simulations, we show that these methods outperform competing methods under a variety of assumptions and demographic models. We apply the new methods to whole-genome human data, and find a number of previously-identified loci with strong evidence of balancing selection, including several HLA genes. Additionally, we find evidence for many novel candidates, the strongest of which is FANK1, an imprinted gene that suppresses apoptosis, is expressed during meiosis in males, and displays marginal signs of segregation distortion. We hypothesize that balancing selection acts on this locus to stabilize the segregation distortion and negative fitness effects of the distorter allele. Thus, our methods are able to reproduce many previously-hypothesized signals of balancing selection, as well as discover novel interesting candidates.     

A Novel Approach for Identifying Causal Models of Complex Diseases from Family Data

Causal models including genetic factors are important for understanding the presentation mechanisms of complex diseases. Familial aggregation and segregation analyses based on polygenic threshold models have been the primary approach to fitting genetic models to the family data of complex diseases. In the current study, an advanced approach to obtaining appropriate causal models for complex diseases based on the sufficient component cause (SCC) model involving combinations of traditional genetics principles was proposed. The probabilities for the entire population, i.e., normal–normal, normal–disease, and disease–disease, were considered for each model for the appropriate handling of common complex diseases. The causal model in the current study included the genetic effects from single genes involving epistasis, complementary gene interactions, gene–environment interactions, and environmental effects. Bayesian inference using a Markov chain Monte Carlo algorithm (MCMC) was used to assess of the proportions of each component for a given population lifetime incidence. This approach is flexible, allowing both common and rare variants within a gene and across multiple genes. An application to schizophrenia data confirmed the complexity of the causal factors. An analysis of diabetes data demonstrated that environmental factors and gene–environment interactions are the main causal factors for type II diabetes. The proposed method is effective and useful for identifying causal models, which can accelerate the development of efficient strategies for identifying causal factors of complex diseases.     

Identifying Metals Contamination in Soil: A Geochemical Approach

The geochemical evaluation methodology described in this paper is used to distinguish contaminated samples from those that contain only naturally occurring levels of inorganic constituents. Site-to-background comparisons of trace elements in soil based solely on statistical techniques are prone to high false positive indications. Trace element distributions in soil tend to span a wide range of concentrations and are highly right-skewed, approximating lognormal distributions, and background data sets are typically too small to capture this range. Geochemical correlations of trace versus major elements are predicated on the natural elemental associations in soil. Linear trends with positive slopes are expected for scatter plots of specific trace versus major elements in uncontaminated samples. Individual samples that may contain a component of contamination are identified by their positions off the trend formed by uncontaminated samples. In addition to pinpointing which samples may be contaminated, this technique provides mechanistic explanations for naturally elevated element concentrations, information that a purely statistical approach cannot provide. These geochemical evaluations have been successfully performed at numerous facilities across the United States. Removing naturally occurring constituents from consideration early in a site investigation reduces or eliminates unnecessary investigation and risk assessment, and focuses remediation efforts.     

cgaTOH: Extended Approach for Identifying Tracts of Homozygosity

Identification of disease variants via homozygosity mapping and investigation of the effects of genome-wide homozygosity regions on traits of biomedical importance have been widely applied recently. Nonetheless, the existing methods and algorithms to identify long tracts of homozygosity (TOH) are not able to provide efficient and rigorous regions for further downstream association investigation. We expanded current methods to identify TOHs by defining “surrogate-TOH”, a region covering a cluster of TOHs with specific characteristics. Our defined surrogate-TOH includes cTOH, viz a common TOH region where at least ten TOHs present; gTOH, whereby a group of highly overlapping TOHs share proximal boundaries; and aTOH, which are allelically-matched TOHs. Searching for gTOH and aTOH was based on a repeated binary spectral clustering algorithm, where a hierarchy of clusters is created and represented by a TOH cluster tree. Based on the proposed method of identifying different species of surrogate-TOH, our cgaTOH software was developed. The software provides an intuitive and interactive visualization tool for better investigation of the high-throughput output with special interactive navigation rings, which will find its applicability in both conventional association studies and more sophisticated downstream analyses. NCBI genome map viewer is incorporated into the system. Moreover, we discuss the choice of implementing appropriate empirical ranges of critical parameters by applying to disease models. This method identifies various patterned clusters of SNPs demonstrating extended homozygosity, thus one can observe different aspects of the multi-faceted characteristics of TOHs.     

FunMod: A Cytoscape Plugin for Identifying Functional Modules in Undirected Protein–Protein Networks

The characterization of the interacting behaviors of complex biological systems is a primary objective in protein–protein network analysis and computational biology. In this paper we present FunMod, an innovative Cytoscape version 2.8 plugin that is able to mine undirected protein–protein networks and to infer sub-networks of interacting proteins intimately correlated with relevant biological pathways. This plugin may enable the discovery of new pathways involved in diseases. In order to describe the role of each protein within the relevant biological pathways, FunMod computes and scores three topological features of the identified sub-networks. By integrating the results from biological pathway clustering and topological network analysis, FunMod proved to be useful for the data interpretation and the generation of new hypotheses in two case studies.     

在结瘤基因nodA启动子内发现了两个不同功能的结构区域

在豌豆根瘤菌（ＲｈｉｚｏｂｉｕｍＬｅｇｕｍｉｎｏｓａｒｕｍ）结瘤基因ｎｏｄＡ的启动子内发现了具有两个不同功能的结构区域,其一我们称为Ｒｉｐ,在ｎｏｄＡ诱导表达中起着关键作用,可能识别经诱导剂作用而发生构象变化的调控蛋白ＮｏｄＤ,另一为ＲＩＰ缺失后留下的,我们称为ＲＰ区,只要ＲＰ存在,不需要诱导剂,ＮｏｄＤ蛋白即能导致结瘤基因ｎｏｄＡ的表达。因此该区可能识别原始构象的调控蛋白ＮｏｄＤ。     

ChIPnorm: A Statistical Method for Normalizing and Identifying Differential Regions in Histone Modification ChIP-seq Libraries

The advent of high-throughput technologies such as ChIP-seq has made possible the study of histone modifications. A problem of particular interest is the identification of regions of the genome where different cell types from the same organism exhibit different patterns of histone enrichment. This problem turns out to be surprisingly difficult, even in simple pairwise comparisons, because of the significant level of noise in ChIP-seq data. In this paper we propose a two-stage statistical method, called ChIPnorm, to normalize ChIP-seq data, and to find differential regions in the genome, given two libraries of histone modifications of different cell types. We show that the ChIPnorm method removes most of the noise and bias in the data and outperforms other normalization methods. We correlate the histone marks with gene expression data and confirm that histone modifications H3K27me3 and H3K4me3 act as respectively a repressor and an activator of genes. Compared to what was previously reported in the literature, we find that a substantially higher fraction of bivalent marks in ES cells for H3K27me3 and H3K4me3 move into a K27-only state. We find that most of the promoter regions in protein-coding genes have differential histone-modification sites. The software for this work can be downloaded from http://lcbb.epfl.ch/software.html.     

Targeted Genome-Wide Enrichment of Functional Regions

Only a small fraction of large genomes such as that of the human contains the functional regions such as the exons, promoters, and polyA sites. A platform technique for selective enrichment of functional genomic regions will enable several next-generation sequencing applications that include the discovery of causal mutations for disease and drug response. Here, we describe a powerful platform technique, termed “functional genomic fingerprinting” (FGF), for the multiplexed genomewide isolation and analysis of targeted regions such as the exome, promoterome, or exon splice enhancers. The technique employs a fixed part of a uniquely designed Fixed-Randomized primer, while the randomized part contains all the possible sequence permutations. The Fixed-Randomized primers bind with full sequence complementarity at multiple sites where the fixed sequence (such as the splice signals) occurs within the genome, and multiplex amplify many regions bounded by the fixed sequences (e.g., exons). Notably, validation of this technique using cardiac myosin binding protein-C (MYBPC3) gene as an example strongly supports the application and efficacy of this method. Further, assisted by genomewide computational analyses of such sequences, the FGF technique may provide a unique platform for high-throughput sample production and analysis of targeted genomic regions by the next-generation sequencing techniques, with powerful applications in discovering disease and drug response genes.