scholarly journals Multiple shifts in gene network interactions shape phenotypes of Drosophila melanogaster selected for long and short night sleep duration

2021 ◽  
Author(s):  
Caetano Souto-Maior ◽  
Yanzhu Lin ◽  
Yazmin Lizette Serrano Negron ◽  
Susan Tracy Harbison
Keyword(s):  
Sleep Duration ◽  
Process Model ◽  
Time Course ◽  
Complex Trait ◽  
Rna Seq ◽  
Full Account ◽  
Data Set ◽  
Hierarchical Generalized Linear Model ◽  
Main Components ◽  
Transcription Data

All but the simplest phenotypes are believed to result from interactions between two or more genes forming complex networks of gene regulation. Sleep is a complex trait known to depend on the system of feedback loops of the circadian clock, and on many other genes; however, the main components regulating the phenotype and how they interact remain an unsolved puzzle. Genomic and transcriptomic data may well provide part of the answer, but a full account requires a suitable quantitative framework. Here we conducted an artificial selection experiment for sleep duration with RNA-seq data acquired each generation. The phenotypic results are robust across replicates and previous experiments, and the transcription data provides a high-resolution, time-course data set for the evolution of sleep-related gene expression. In addition to a Hierarchical Generalized Linear Model analysis of differential expression that accounts for experimental replicates we develop a flexible Gaussian Process model that estimates interactions between genes. 145 gene pairs are found to have interactions that are different from controls. Our method not only is considerably more specific than standard correlation metrics but also more sensitive, finding correlations not significant by other methods. Statistical predictions were compared to experimental data from public databases on gene interactions.

scholarly journals 72-h diurnal RNA-seq analysis of fully expanded third leaves from maize, sorghum, and foxtail millet at 3-h resolution

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Xianjun Lai ◽  
Claire Bendix ◽  
Yang Zhang ◽  
James C. Schnable ◽  
Frank G. Harmon
Keyword(s):  
Gene Expression ◽  
Network Architecture ◽  
Time Course ◽  
Foxtail Millet ◽  
Transcriptome Profiling ◽  
Rna Seq ◽  
Data Set ◽  
Continuous Sampling ◽  
Information Sequence ◽  
Metabolic Activities

Abstract Objectives The purpose of this data set is to capture the complete diurnal (i.e., daily) transcriptome of fully expanded third leaves from the C4 panacoid grasses sorghum (Sorghum bicolor), maize (Zea mays), and foxtail millet (Setaria italica) with RNA-seq transcriptome profiling. These data are the cornerstone of a larger project that examined the conservation and divergence of gene expression networks within these crop plants. This data set focuses on temporal changes in gene expression to identify the network architecture responsible for daily regulation of plant growth and metabolic activities. The power of this data set is fine temporal resolution combined with continuous sampling over multiple days. Data description The data set is 72 individual RNA-seq samples representing 24 time course samples each for sorghum, maize, and foxtail millet plants cultivated in a growth chamber under equal intervals of light and darkness. The 24 samples are separated by 3-h intervals so that the data set is a fine scale 72-h analysis of gene expression in the leaves of each plant type. FASTQ files from Illumina sequencing are available at the National Center for Biotechnology Information Sequence Read Archive.

scholarly journals SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Verônica R. de Melo Costa ◽  
Julianus Pfeuffer ◽  
Annita Louloupi ◽  
Ulf A. V. Ørom ◽  
Rosario M. Piro
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Time Course ◽  
Progressive Increase ◽  
Rna Seq ◽  
Transcript Processing ◽  
Aberrant Transcript ◽  
Genome Wide ◽  
Splicing Efficiency ◽  
Nascent Rna

Abstract Background Introns are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Thus, to better understand the dynamics of this process and the perturbations that might be caused by aberrant transcript processing it is important to quantify splicing efficiency. Results Here, we introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. SPLICE-q uses aligned reads from strand-specific RNA-seq to quantify splicing efficiency for each intron individually and allows the user to select different levels of restrictiveness concerning the introns’ overlap with other genomic elements such as exons of other genes. We applied SPLICE-q to globally assess the dynamics of intron excision in yeast and human nascent RNA-seq. We also show its application using total RNA-seq from a patient-matched prostate cancer sample. Conclusions Our analyses illustrate that SPLICE-q is suitable to detect a progressive increase of splicing efficiency throughout a time course of nascent RNA-seq and it might be useful when it comes to understanding cancer progression beyond mere gene expression levels. SPLICE-q is available at: https://github.com/vrmelo/SPLICE-q

scholarly journals Effect of Speech-to-Noise Ratio and Luminance on a Range of Current and Potential Pupil Response Measures to Assess Listening Effort

2021 ◽  
Vol 25 ◽  
pp. 233121652110093
Author(s):  
Patrycja Książek ◽  
Adriana A. Zekveld ◽  
Dorothea Wendt ◽  
Lorenz Fiedler ◽  
Thomas Lunner ◽  
...  
Keyword(s):  
Time Course ◽  
Principal Component ◽  
Growth Curve Analysis ◽  
Listening Effort ◽  
Data Set ◽  
Holistic View ◽  
Hearing Research ◽  
Speech In Noise ◽  
Noise Test ◽  
Noise Ratio

In hearing research, pupillometry is an established method of studying listening effort. The focus of this study was to evaluate several pupil measures extracted from the Task-Evoked Pupil Responses (TEPRs) in speech-in-noise test. A range of analysis approaches was applied to extract these pupil measures, namely (a) pupil peak dilation (PPD); (b) mean pupil dilation (MPD); (c) index of pupillary activity; (d) growth curve analysis (GCA); and (e) principal component analysis (PCA). The effect of signal-to-noise ratio (SNR; Data Set A: –20 dB, –10 dB, +5 dB SNR) and luminance (Data Set B: 0.1 cd/m2, 360 cd/m2) on the TEPRs were investigated. Data Sets A and B were recorded during a speech-in-noise test and included TEPRs from 33 and 27 normal-hearing native Dutch speakers, respectively. The main results were as follows: (a) A significant effect of SNR was revealed for all pupil measures extracted in the time domain (PPD, MPD, GCA, PCA); (b) Two time series analysis approaches (GCA, PCA) provided modeled temporal profiles of TEPRs (GCA); and time windows spanning subtasks performed in a speech-in-noise test (PCA); and (c) All pupil measures revealed a significant effect of luminance. In conclusion, multiple pupil measures showed similar effects of SNR, suggesting that effort may be reflected in multiple aspects of TEPR. Moreover, a direct analysis of the pupil time course seems to provide a more holistic view of TEPRs, yet further research is needed to understand and interpret its measures. Further research is also required to find pupil measures less sensitive to changes in luminance.

Tuning of a Basic Coordination Pattern Constructs Straight-Ahead and Curved Walking in Humans

2004 ◽  
Vol 91 (4) ◽  
pp. 1524-1535 ◽  
Author(s):  
Grégoire Courtine ◽  
Marco Schieppati
Keyword(s):  
Principal Components ◽  
Lower Limb ◽  
Time Course ◽  
Body Movement ◽  
The Body ◽  
Whole Body ◽  
Coordination Pattern ◽  
Data Set ◽  
Coordination Patterns ◽  
Straight Ahead

We tested the hypothesis that common principles govern the production of the locomotor patterns for both straight-ahead and curved walking. Whole body movement recordings showed that continuous curved walking implies substantial, limb-specific changes in numerous gait descriptors. Principal component analysis (PCA) was used to uncover the spatiotemporal structure of coordination among lower limb segments. PCA revealed that the same kinematic law accounted for the coordination among lower limb segments during both straight-ahead and curved walking, in both the frontal and sagittal planes: turn-related changes in the complex behavior of the inner and outer limbs were captured in limb-specific adaptive tuning of coordination patterns. PCA was also performed on a data set including all elevation angles of limb segments and trunk, thus encompassing 13 degrees of freedom. The results showed that both straight-ahead and curved walking were low dimensional, given that 3 principal components accounted for more than 90% of data variance. Furthermore, the time course of the principal components was unchanged by curved walking, thereby indicating invariant coordination patterns among all body segments during straight-ahead and curved walking. Nevertheless, limb- and turn-dependent tuning of the coordination patterns encoded the adaptations of the limb kinematics to the actual direction of the walking body. Absence of vision had no significant effect on the intersegmental coordination during either straight-ahead or curved walking. Our findings indicate that kinematic laws, probably emerging from the interaction of spinal neural networks and mechanical oscillators, subserve the production of both straight-ahead and curved walking. During locomotion, the descending command tunes basic spinal networks so as to produce the changes in amplitude and phase relationships of the spinal output, sufficient to achieve the body turn.

scholarly journals Assessment of the Metabolic Pathways Associated With Glucose-Stimulated Biphasic Insulin Secretion

Endocrinology ◽  
2014 ◽  
Vol 155 (5) ◽  
pp. 1653-1666 ◽  
Author(s):  
Mei Huang ◽  
Jamie W. Joseph
Keyword(s):  
Insulin Secretion ◽  
Time Course ◽  
Tricarboxylic Acid Cycle ◽  
Global Analysis ◽  
Tricarboxylic Acid ◽  
Second Phase ◽  
Data Set ◽  
Negatively Associated ◽  
Biphasic Insulin ◽  
Biphasic Insulin Secretion

Biphasic glucose-stimulated insulin secretion involves a rapid first phase followed by a prolonged second phase of insulin secretion. The biochemical pathways that control these 2 phases of insulin secretion are poorly defined. In this study, we used a gas chromatography mass spectroscopy-based metabolomics approach to perform a global analysis of cellular metabolism during biphasic insulin secretion. A time course metabolomic analysis of the clonal β-cell line 832/13 cells showed that glycolytic, tricarboxylic acid, pentose phosphate pathway, and several amino acids were strongly correlated to biphasic insulin secretion. Interestingly, first-phase insulin secretion was negatively associated with l-valine, trans-4-hydroxy-l-proline, trans-3-hydroxy-l-proline, dl-3-aminoisobutyric acid, l-glutamine, sarcosine, l-lysine, and thymine and positively with l-glutamic acid, flavin adenine dinucleotide, caprylic acid, uridine 5′-monophosphate, phosphoglycerate, myristic acid, capric acid, oleic acid, linoleic acid, and palmitoleic acid. Tricarboxylic acid cycle intermediates pyruvate, α-ketoglutarate, and succinate were positively associated with second-phase insulin secretion. Other metabolites such as myo-inositol, cholesterol, dl-3-aminobutyric acid, and l-norleucine were negatively associated metabolites with the second-phase of insulin secretion. These studies provide a detailed analysis of key metabolites that are either negatively or positively associated with biphasic insulin secretion. The insights provided by these data set create a framework for planning future studies in the assessment of the metabolic regulation of biphasic insulin secretion.

scholarly journals Time-Course Transcriptome Analysis Reveals Resistance Genes of Panax ginseng Induced by Cylindrocarpon destructans Infection Using RNA-Seq

PLoS ONE ◽  
2016 ◽  
Vol 11 (2) ◽  
pp. e0149408 ◽  
Author(s):  
Yuan Gao ◽  
Xiaoli He ◽  
Bin Wu ◽  
Qiliang Long ◽  
Tianwei Shao ◽  
...  
Keyword(s):  
Panax Ginseng ◽  
Resistance Genes ◽  
Transcriptome Analysis ◽  
Time Course ◽  
Rna Seq ◽  
Cylindrocarpon Destructans

scholarly journals Male and Female Partner-Caregivers’ Burden: Does It Get Worse Over Time?

2018 ◽  
Vol 59 (6) ◽  
pp. 1103-1111 ◽  
Author(s):  
Joukje C Swinkels ◽  
Marjolein I Broese van Groenou ◽  
Alice de Boer ◽  
Theo G van Tilburg
Keyword(s):  
Caregiver Burden ◽  
Process Model ◽  
Female Partner ◽  
General View ◽  
Data Set ◽  
Change Over Time ◽  
Men And Women ◽  
Wear And Tear ◽  
The Impact ◽  
Over Time

Abstract Background and Objectives The general view is that partner-caregiver burden increases over time but findings are inconsistent. Moreover, the pathways underlying caregiver burden may differ between men and women. This study examines to what degree and why partner-caregiver burden changes over time. It adopts Pearlin’s Caregiver Stress Process Model, as it is expected that higher primary and secondary stressors will increase burden and larger amounts of resources will lower burden. Yet, the impact of stressors and resources may change over time. The wear-and-tear model predicts an increase of burden due to a stronger impact of stressors and lower impact of resources over time. Alternatively, the adaptation model predicts a decrease of burden due to a lower impact of stressors and higher impact of resources over time. Research Design and Methods We used 2 observations with a 1-year interval of 279 male and 443 female partner-caregivers, derived from the Netherlands Older Persons and Informal Caregivers Survey Minimum Data Set. We applied multilevel regression analysis, stratified by gender. Results Adjusted for all predictors, caregiver burden increased over time for both men and women. For female caregivers, the impact of poor spousal health on burden increased and the impact of fulfillment decreased over time. Among male caregivers, the impact of predictors did not change over time. Discussion and Implications The increase of burden over time supports the wear-and-tear model, in particular for women. This study highlights the need for gender-specific interventions that are focused on enabling older partners to be better prepared for long-term partner-care.

scholarly journals High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability

2018 ◽  
Author(s):  
Pier Francesco Palamara ◽  
Jonathan Terhorst ◽  
Yun S. Song ◽  
Alkes L. Price
Keyword(s):  
Positive Selection ◽  
Snp Array ◽  
Complex Trait ◽  
Joint Analysis ◽  
Background Selection ◽  
Sequencing Data ◽  
Data Set ◽  
Coalescence Time ◽  
Recent Positive Selection ◽  
Coalescence Times

AbstractInterest in reconstructing demographic histories has motivated the development of methods to estimate locus-specific pairwise coalescence times from whole-genome sequence data. We developed a new method, ASMC, that can estimate coalescence times using only SNP array data, and is 2-4 orders of magnitude faster than previous methods when sequencing data are available. We were thus able to apply ASMC to 113,851 phased British samples from the UK Biobank, aiming to detect recent positive selection by identifying loci with unusually high density of very recent coalescence times. We detected 12 genome-wide significant signals, including 6 loci with previous evidence of positive selection and 6 novel loci, consistent with coalescent simulations showing that our approach is well-powered to detect recent positive selection. We also applied ASMC to sequencing data from 498 Dutch individuals (Genome of the Netherlands data set) to detect background selection at deeper time scales. We observed highly significant correlations between average coalescence time inferred by ASMC and other measures of background selection. We investigated whether this signal translated into an enrichment in disease and complex trait heritability by analyzing summary association statistics from 20 independent diseases and complex traits (average N=86k) using stratified LD score regression. Our background selection annotation based on average coalescence time was strongly enriched for heritability (p = 7×10−153) in a joint analysis conditioned on a broad set of functional annotations (including other background selection annotations), meta-analyzed across traits; SNPs in the top 20% of our annotation were 3.8x enriched for heritability compared to the bottom 20%. These results underscore the widespread effects of background selection on disease and complex trait heritability.

scholarly journals Mixture models reveal multiple positional bias types in RNA-Seq data and lead to accurate transcript concentration estimates

2014 ◽  
Author(s):  
Andreas Tuerk ◽  
Gregor Wiktorin ◽  
Serhat Güler
Keyword(s):  
Probability Distributions ◽  
False Positive Rate ◽  
Synthetic Data ◽  
True Positive Rate ◽  
Rna Seq ◽  
Microarray Quality Control ◽  
Data Set ◽  
Rna Transcripts ◽  
Positive Rate ◽  
Fragment Distribution

Quantification of RNA transcripts with RNA-Seq is inaccurate due to positional fragment bias, which is not represented appropriately by current statistical models of RNA-Seq data. This article introduces the Mix2(rd. "mixquare") model, which uses a mixture of probability distributions to model the transcript specific positional fragment bias. The parameters of the Mix2model can be efficiently trained with the Expectation Maximization (EM) algorithm resulting in simultaneous estimates of the transcript abundances and transcript specific positional biases. Experiments are conducted on synthetic data and the Universal Human Reference (UHR) and Brain (HBR) sample from the Microarray quality control (MAQC) data set. Comparing the correlation between qPCR and FPKM values to state-of-the-art methods Cufflinks and PennSeq we obtain an increase in R2value from 0.44 to 0.6 and from 0.34 to 0.54. In the detection of differential expression between UHR and HBR the true positive rate increases from 0.44 to 0.71 at a false positive rate of 0.1. Finally, the Mix2model is used to investigate biases present in the MAQC data. This reveals 5 dominant biases which deviate from the common assumption of a uniform fragment distribution. The Mix2software is available at http://www.lexogen.com/fileadmin/uploads/bioinfo/mix2model.tgz.

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