Non-linear Physiology and Gene Expression Responses of Harmful Alga Heterosigma akashiwo to Rising CO2

Protist ◽  
2019 ◽  
Vol 170 (1) ◽  
pp. 38-51 ◽  
Author(s):  
Gwenn M.M. Hennon ◽  
Olivia M. Williamson ◽  
María D. Hernández Limón ◽  
Sheean T. Haley ◽  
Sonya T. Dyhrman
Keyword(s):  
Gene Expression ◽  
Heterosigma Akashiwo ◽  
Harmful Alga ◽  
Non Linear

scholarly journals Behavioral and Physiological Changes during Benthic-Pelagic Transition in the Harmful Alga, Heterosigma akashiwo: Potential for Rapid Bloom Formation

PLoS ONE ◽  
2013 ◽  
Vol 8 (10) ◽  
pp. e76663 ◽  
Author(s):  
Elizabeth D. Tobin ◽  
Daniel Grünbaum ◽  
Johnathan Patterson ◽  
Rose Ann Cattolico
Keyword(s):  
Heterosigma Akashiwo ◽  
Physiological Changes ◽  
Harmful Alga ◽  
Bloom Formation

scholarly journals Novel analysis of drug-drug interactions reveals the appearance of non-linear dose dependence under the single drug treatment

2020 ◽  
Author(s):  
Yh. Taguchi ◽  
Turki Turki
Keyword(s):  
Gene Expression ◽  
Drug Interactions ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Tensor Decomposition ◽  
Dose Dependence ◽  
Linear Dose ◽  
Non Linear ◽  
New Interactions ◽  
Dose Dependent

ABSTRACTThe accurate prediction of new interactions between drugs is important for avoiding unknown (mild or severe) adverse reactions to drug combinations. The development of effective in silico methods for evaluating drug interactions based on gene expression data requires an under-standing of how various drugs alter gene expression. Current computational methods for the prediction of drug-drug interactions (DDIs) utilize data for known DDIs to predict unknown interactions. However, these methods are limited in the absence of known predictive DDIs. To improve DDIs’ interpretation, a recent study has demonstrated strong non-linear (i.e., dose-dependent) effects of DDIs. In this study, we present a new unsupervised learning approach involving tensor decomposition (TD)-based unsupervised feature extraction (FE) in 3D. We utilize our approach to reanalyze available gene expression profiles for Saccharomyces cerevisiae. We found that non-linearity is possible, even for single drugs. Thus, non-linear dose-dependence cannot always be attributed to DDIs. Our analysis provides a basis for the design of effective methods for evaluating DDIs.

SYSTEMATIC VARIATION NORMALIZATION IN MICROARRAY DATA TO GET GENE EXPRESSION COMPARISON UNBIASED

2005 ◽  
Vol 03 (02) ◽  
pp. 225-241 ◽  
Author(s):  
JEFF W. CHOU ◽  
RICHARD S. PAULES ◽  
PIERRE R. BUSHEL
Keyword(s):  
Gene Expression ◽  
Linear Regression ◽  
Microarray Data ◽  
Expression Patterns ◽  
Microarray Gene Expression Data ◽  
Systematic Variation ◽  
Data Sets ◽  
Microarray Gene Expression ◽  
Pixel Intensity ◽  
Non Linear

Normalization removes or minimizes the biases of systematic variation that exists in experimental data sets. This study presents a systematic variation normalization (SVN) procedure for removing systematic variation in two channel microarray gene expression data. Based on an analysis of how systematic variation contributes to variability in microarray data sets, our normalization procedure includes background subtraction determined from the distribution of pixel intensity values from each data acquisition channel and log conversion, linear or non-linear regression, restoration or transformation, and multiarray normalization. In the case when a non-linear regression is required, an empirical polynomial approximation approach is used. Either the high terminated points or their averaged values in the distributions of the pixel intensity values observed in control channels may be used for rescaling multiarray datasets. These pre-processing steps remove systematic variation in the data attributable to variability in microarray slides, assay-batches, the array process, or experimenters. Biologically meaningful comparisons of gene expression patterns between control and test channels or among multiple arrays are therefore unbiased using normalized but not unnormalized datasets.

Abstract 1006: Non-linear relationship between estradiol-regulated changes in chromatin architecture and gene expression

2019 ◽  
Author(s):  
Taylor M. Parker ◽  
Duojiao Chen ◽  
Poornima Bhat-Nakshatri ◽  
Xiaona Chu ◽  
Yunlong Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Linear Relationship ◽  
Chromatin Architecture ◽  
Non Linear

scholarly journals NetExtractor: Extracting a Cerebellar Tissue Gene Regulatory Network Using Differentially Expressed High Mutual Information Binary RNA Profiles

2020 ◽  
Vol 10 (9) ◽  
pp. 2953-2963
Author(s):  
Benafsh Husain ◽  
Allison R Hickman ◽  
Yuqing Hang ◽  
Benjamin T Shealy ◽  
Karan Sapra ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mutual Information ◽  
Brain Tissue ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Cerebellar Hemisphere ◽  
Biologically Relevant ◽  
Non Linear ◽  
Cerebellar Tissue ◽  
Gene Regulatory

Abstract Bigenic expression relationships are conventionally defined based on metrics such as Pearson or Spearman correlation that cannot typically detect latent, non-linear dependencies or require the relationship to be monotonic. Further, the combination of intrinsic and extrinsic noise as well as embedded relationships between sample sub-populations reduces the probability of extracting biologically relevant edges during the construction of gene co-expression networks (GCNs). In this report, we address these problems via our NetExtractor algorithm. NetExtractor examines all pairwise gene expression profiles first with Gaussian mixture models (GMMs) to identify sample sub-populations followed by mutual information (MI) analysis that is capable of detecting non-linear differential bigenic expression relationships. We applied NetExtractor to brain tissue RNA profiles from the Genotype-Tissue Expression (GTEx) project to obtain a brain tissue specific gene expression relationship network centered on cerebellar and cerebellar hemisphere enriched edges. We leveraged the PsychENCODE pre-frontal cortex (PFC) gene regulatory network (GRN) to construct a cerebellar cortex (cerebellar) GRN associated with transcriptionally active regions in cerebellar tissue. Thus, we demonstrate the utility of our NetExtractor approach to detect biologically relevant and novel non-linear binary gene relationships.

scholarly journals Identification of A 10-Gene Signature To Predict Efficacy of Neoadjuvant Therapy in Patients With HER2 Positive Breast Cancer.

2021 ◽  
Author(s):  
Yusong Wang ◽  
Mozhi Wang ◽  
Xiangyu Sun ◽  
Litong Yao ◽  
Mengshen Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Neoadjuvant Therapy ◽  
Spline Regression ◽  
Her2 Positive ◽  
Immune Infiltration ◽  
Her2 Positive Breast Cancer ◽  
Non Linear ◽  
Er Status ◽  
Positive Breast Cancer

Abstract Background:Patients with human epidermal growth factor receptor 2 (HER2) positive breast cancer represent a poor prognosis, which are recommended to be treated with neoadjuvant therapy (NAT). Tumor immune microenvironment, especially tumor infiltrating cells (TILs), are proved to predict the efficacy of NAT. However, validated immune-related multi-gene signatures for HER2-positive BC are still lacking.Methods:We collected gene expression arrays of pre-NAT samples from the National Center for Biotechnology Information Gene Expression Omnibus. Totally 4 studies are included in our study (n=295, no. of train=207, no. of validation=95) to construct the signature. Single Sample Gene Set Enrichment Analysis (ssGSEA)and weighted gene co-expression network analysis (WGCNA)were used to quantify immune-infiltrating components in tumor environment and to identify immune related modules. We used spline regression to evaluate non-linear effect of genes and to construct the signature.Results:Immune infiltration status was significantly related to pathological complete response (pCR) (p=0.02). We filtered 80 differential expression genes according to immune infiltration status, and identified two gene modules correlated to pCR and immune infiltration status. CCL5, CD72, PTGDS, CYTIP, PAX5, and estrogen receptor (ER)status were significantly related to pCR in linear multivariate analysis. In spline regression, non-linear aspects of MAP7, IL2RB, CD3G, PTPRC, TRAC were relevant to pCR. We constructed a signature concerning both linear and non-linear effects of genes, which was validated in 5-fold cross validation (AUC=0.81) and an external validation cohort (n=88) (AUC=0.797).Conclusions:In HER2 positive BC, immune infiltration status should be involved into consideration to make optimal regimens. A ten-gene generalized non-linear signature including ER status could predict the efficacy of NAT.

scholarly journals  ALUminating the Path of Atherosclerosis Progression: Chaos Theory Suggests a Role for Alu Repeats in the Development of Atherosclerotic Vascular Disease

2018 ◽  
Author(s):  
Miguel Hueso ◽  
Josep M Cruzado ◽  
Joan Torras ◽  
Estanis Navarro
Keyword(s):  
Gene Expression ◽  
Human Genome ◽  
Chaos Theory ◽  
Molecular Mechanisms ◽  
Regulation Of Gene Expression ◽  
Structural Constraints ◽  
Linear Dynamical Systems ◽  
Alu Elements ◽  
Non Linear ◽  
The Impact

Atherosclerosis (ATH) and Coronary Artery Disease (CAD) are chronic inflammatory diseases with an important genetic background which derive from the cumulative effect of multiple common risk alleles, most of them located in genomic non-coding regions. These complex diseases behave as non-linear dynamical systems that show a high dependence on their initial conditions, so that long-term predictions of disease progression are unreliable. One likely possibility is that the non-linear nature of ATH could be dependent on non-linear correlations in the structure of the human genome. In this review we show how Chaos theory analysis highlighted genomic regions that shared specific structural constraints that could have a role in ATH progression. These regions were shown to be enriched in repetitive sequences of the Alu family, genomic parasites which colonized the human genome, which show a particular secondary structure and have been involved in the regulation of gene expression. We also review the impact of Alu elements on the mechanisms that regulate gene expression, especially highlighting the molecular mechanisms by which the Alu elements could alter the inflammatory homeostasis. We devise especial attention to their relationship with the lncRNA ANRIL, the strongest risk factor for ATH, their role as miRNA sponges, and their ability to interfere with the regulatory circuitry of the NF-kB response. We aim to characterize ATH as a non-linear dynamic system in which small initial alterations in the expression of a number of repetitive elements are somehow amplified to reach phenotypic significance.

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