A gene coexpression network for bovine skeletal muscle inferred from microarray data

2006 ◽  
Vol 28 (1) ◽  
pp. 76-83 ◽  
Author(s):  
Antonio Reverter ◽  
Nicholas J. Hudson ◽  
Yonghong Wang ◽  
Siok-Hwee Tan ◽  
Wes Barris ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Large Scale ◽  
Mixed Model ◽  
Structural Proteins ◽  
Joint Analysis ◽  
Experimental Conditions ◽  
Model Equations ◽  
Highly Correlated ◽  
Bovine Skeletal Muscle ◽  
Fast Twitch

We present the application of large-scale multivariate mixed-model equations to the joint analysis of nine gene expression experiments in beef cattle muscle and fat tissues with a total of 147 hybridizations, and we explore 47 experimental conditions or treatments. Using a correlation-based method, we constructed a gene network for 822 genes. Modules of muscle structural proteins and enzymes, extracellular matrix, fat metabolism, and protein synthesis were clearly evident. Detailed analysis of the network identified groupings of proteins on the basis of physical association. For example, expression of three components of the z-disk, MYOZ1, TCAP, and PDLIM3, was significantly correlated. In contrast, expression of these z-disk proteins was not highly correlated with the expression of a cluster of thick (myosins) and thin (actin and tropomyosins) filament proteins or of titin, the third major filament system. However, expression of titin was itself not significantly correlated with the cluster of thick and thin filament proteins and enzymes. Correlation in expression of many fast-twitch muscle structural proteins and enzymes was observed, but slow-twitch-specific proteins were not correlated with the fast-twitch proteins or with each other. In addition, a number of significant associations between genes and transcription factors were also identified. Our results not only recapitulate the known biology of muscle but have also started to reveal some of the underlying associations between and within the structural components of skeletal muscle.

Construction of gene interaction and regulatory networks in bovine skeletal muscle from expression data

2005 ◽  
Vol 45 (8) ◽  
pp. 821 ◽  
Author(s):  
A. Reverter ◽  
W. Barris ◽  
N. Moreno-Sánchez ◽  
S. McWilliam ◽  
Y. H. Wang ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Transcription Factors ◽  
Regulatory Networks ◽  
Mixed Model ◽  
Gene Interaction ◽  
Expression Data ◽  
Experimental Conditions ◽  
The Usa ◽  
Biological Interpretation ◽  
Bovine Skeletal Muscle

We propose a data-driven reverse engineering approach to isolate the components of a gene interaction and regulatory network. We apply this method to the construction of a network for bovine skeletal muscle. Key nodes in the network include muscle-specific genes and transcription factors. muscle-specific genes are identified from data mining the USA National Cancer Institute, Cancer Genome Anatomy Project database, while transcription factors are predicted by accurate function annotation. A total of 5 microarray studies spanning 78 hybridisations and 23 different experimental conditions provided raw expression data. A recently-reported analytical method based on multivariate mixed-model equations is used to compute gene co-expression measures across 624 genes. The resulting network included 102 genes (of which 40 were muscle-specific genes and 7 were transcription factors) that clustered in 7 distinct modules with clear biological interpretation.

pH dependence of myosin binding-induced activation of the thin filament in cardiac myocytes and skeletal fibers

1996 ◽  
Vol 270 (3) ◽  
pp. H1008-H1014 ◽  
Author(s):  
J. M. Metzger
Keyword(s):  
Skeletal Muscle ◽  
Cardiac Myocytes ◽  
Thin Filament ◽  
Ph Dependence ◽  
Muscle Fibers ◽  
Isometric Tension ◽  
Experimental Conditions ◽  
Skeletal Muscle Fibers ◽  
Myosin Binding ◽  
Fast Twitch

The pH dependence of myosin binding-induced thin filament activation was determined in permeabilized cardiac myocytes and slow- and fast-twitch single skeletal muscle fibers by experimental lowering of [MgATP] in the Ca(2+)-free solutions bathing the permeabilized preparations. As the pS (where S is [MgATP] and pS is -log[MgATP]) was increased from 3.0 to 8.0, isometric tension increased to a peak value in the pS range of 4.9-5.3. At pH 7.00, the transition from the relaxed to the activated rigor state was steep in cardiac myocytes [Hill value (nH) = 21.2 +/- 3.1 (SE)] and due to the apparent effect of strongly bound cross bridges to cooperatively activate the thin filament in the absence of added Ca2+. At pH 6.20, the steepness of the tension-pS relationship was markedly reduced (nH = 6.1 +/- 1.0) and the midpoint of the relationship (pS50) was shifted to higher pS values in cardiac myocytes. In comparison, reduced pH had no effect on the steepness or position of the tension-pS relationship in single slow- or fast-twitch skeletal muscle fibers. These findings suggest that myosin binding-induced activation of the thin filament is pH dependent in cardiac myocytes but not in skeletal muscle fibers under these experimental conditions in which Ca2+ is absent.

scholarly journals Multivariate Identification of Functional Neural Networks Underpinning Humorous Movie Viewing

2021 ◽  
Vol 11 ◽  
Author(s):  
Fa-Hsuan Lin ◽  
Hsin-Ju Lee ◽  
Wen-Jui Kuo ◽  
Iiro P. Jääskeläinen
Keyword(s):  
Large Scale ◽  
Brain Structures ◽  
Brain Network ◽  
Network Activity ◽  
Joint Analysis ◽  
Behavioral Measures ◽  
Experimental Conditions ◽  
Free Viewing ◽  
Fmri Data Analysis ◽  
Temporal Models

While univariate functional magnetic resonance imaging (fMRI) data analysis methods have been utilized successfully to map brain areas associated with cognitive and emotional functions during viewing of naturalistic stimuli such as movies, multivariate methods might provide the means to study how brain structures act in concert as networks during free viewing of movie clips. Here, to achieve this, we generalized the partial least squares (PLS) analysis, based on correlations between voxels, experimental conditions, and behavioral measures, to identify large-scale neuronal networks activated during the first time and repeated watching of three ∼5-min comedy clips. We identified networks that were similarly activated across subjects during free viewing of the movies, including the ones associated with self-rated experienced humorousness that were composed of the frontal, parietal, and temporal areas acting in concert. In conclusion, the PLS method seems to be well suited for the joint analysis of multi-subject neuroimaging and behavioral data to quantify a functionally relevant brain network activity without the need for explicit temporal models.

scholarly journals A Shortcut Approach for Large-scale Mixed Model Associations with Binary Traits

2021 ◽  
Author(s):  
Runqing Yang ◽  
Jun Bao ◽  
Runqing Yang ◽  
Yuxin Song ◽  
Zhiyu Hao ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Multiple Testing ◽  
Statistical Power ◽  
Large Scale ◽  
Mixed Model ◽  
Joint Analysis ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Genomic Control ◽  
Genomic Heritability

Abstract Generalized linear mixed models exhibit computationally intensive and biasness in mapping quantitative trait nucleotides for binary diseases. In genomic logit regression, we consider genomic breeding values estimated in advance as a known predictor, and then correct the deflated association test statistics by using genomic control, thereby successfully extending GRAMMAR-Lambda to analyze binary diseases in a complex structured population. Because there is no need to estimate genomic heritability and genomic breeding values can be estimated by a small number of sampling markers, the generalized mixed-model association analysis has been extremely simplified to handle large-scale data. With almost perfect genomic control, joint analysis for the candidate quantitative trait nucleotides chosen by multiple testing offered a significant improvement in statistical power.

Glycogenesis and glyconeogenesis in skeletal muscle: effects of pH and hormones

1990 ◽  
Vol 258 (4) ◽  
pp. E693-E700 ◽  
Author(s):  
A. Bonen ◽  
J. C. McDermott ◽  
M. H. Tan
Keyword(s):  
Skeletal Muscle ◽  
Soleus Muscle ◽  
Extensor Digitorum Longus ◽  
Muscle Fibers ◽  
Extensor Digitorum ◽  
Effects Of Ph ◽  
Slow Twitch ◽  
Highly Correlated ◽  
Fast Twitch

We examined the effects of selected hormones and pH on the rates of glyconeogenesis (L-[U-14C]-lactate----glycogen) and glycogenesis (D-[U-14C]glucose----glycogen) in mouse fast-twitch (FT) and slow-twitch muscles incubated in vitro (37 degrees C). Glyconeogenesis and glycogenesis increased linearly with increasing concentrations of lactate (5-20 mM) and glucose (2.5-10 mM), respectively, in both muscles. Glyconeogenesis was approximately three- to fourfold greater in the extensor digitorum longus (EDL) than in the soleus, whereas basal glycogenesis was twofold greater in the soleus muscle than in the EDL. Lactate accounted for up to 5% of the glycogen formed in the soleus and up to 32% in the EDL relative to the rates of glycogenesis (i.e., 5 mM glucose + 10 nM insulin) in each muscle. Corticosterone (10(-12)-10(-6) M) failed to alter glyconeogenesis, whereas this hormone reduced glycogenesis. Insulin (10 nM) markedly stimulated glycogenesis but failed to stimulate glyconeogenesis. The rates of both glycogenesis and glyconeogenesis were pH sensitive, with optimal rates at pH 6.5-7.0 in both muscles. Glyconeogenesis increased by 49% in the soleus and by 39% EDL at pH 6.5 compared with pH 7.4. Glycogenesis increased in the soleus (SOL) and EDL in the absence (SOL: +22%; EDL: +52%) and presence of insulin (SOL: +22%; EDL: +51%) at pH 6.5 when compared with pH 7.4. In additional experiments with the perfused rat hindquarter, rates of glyconeogenesis were shown to be highly correlated with proportion of FT muscle fibers in a muscle.(ABSTRACT TRUNCATED AT 250 WORDS)

scholarly journals A Shortcut Approach for Large-scale Mixed Model Associations with Binary Traits

2021 ◽  
Author(s):  
Runqing Yang ◽  
Jun Bao ◽  
Runqing Yang ◽  
Yuxin Song ◽  
Zhiyu Hao ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Multiple Testing ◽  
Statistical Power ◽  
Large Scale ◽  
Mixed Model ◽  
Joint Analysis ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Genomic Control ◽  
Genomic Heritability

Abstract Generalized linear mixed models exhibit computationally intensive and biasness in mapping quantitative trait nucleotides for binary diseases. In genomic logit regression, we consider genomic breeding values estimated in advance as a known predictor, and then correct the deflated association test statistics by using genomic control, thereby successfully extending GRAMMAR-Lambda to analyze binary diseases in a complex structured population. Because there is no need to estimate genomic heritability and genomic breeding values can be estimated by a small number of sampling markers, the generalized mixed-model association analysis has been extremely simplified to handle large-scale data. With almost perfect genomic control, joint analysis for the candidate quantitative trait nucleotides chosen by multiple testing offered a significant improvement in statistical power.

scholarly journals Evidence for membrane microheterogeneity in the sarcoplasmic reticulum of fast twitch skeletal muscle.

1982 ◽  
Vol 257 (19) ◽  
pp. 11689-11695
Author(s):  
W B Van Winkle ◽  
R J Bick ◽  
D E Tucker ◽  
C A Tate ◽  
M L Entman
Keyword(s):  
Skeletal Muscle ◽  
Sarcoplasmic Reticulum ◽  
Fast Twitch ◽  
Membrane Microheterogeneity

scholarly journals MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Daniel E. Runcie ◽  
Jiayi Qu ◽  
Hao Cheng ◽  
Lorin Crawford
Keyword(s):  
Genomic Prediction ◽  
Large Scale ◽  
Mixed Model ◽  
Human Genetics ◽  
Linear Mixed Effect Model ◽  
Mixed Effect ◽  
Statistical Framework ◽  
Effect Model ◽  
Plant Data ◽  
Genetic Value

AbstractLarge-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present , a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.

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