scholarly journals Global transcriptome analysis of theMyxococcus xanthusmulticellular developmental program

2019 ◽  
Author(s):  
J. Muñoz-Dorado ◽  
A. Moraleda-Muñoz ◽  
F.J. Marcos-Torres ◽  
F.J. Contreras-Moreno ◽  
A.B. Martin-Cuadrado ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Myxococcus Xanthus ◽  
Gene Expression Profiles ◽  
Global Gene Expression ◽  
Developmental Cycle ◽  
Fruiting Bodies ◽  
Rna Seq ◽  
Developmental Program ◽  
Specific Time Point ◽  
Global Transcriptome

ABSTRACTThe bacteriaMyxococcus xanthusexhibit a complex multicellular life cycle. In the presence of nutrients, cells prey cooperatively. Upon starvation, they enter a developmental cycle wherein cells aggregate to produce macroscopic fruiting bodies filled with resistant myxospores. We used RNA-Seq technology to examine the global transcriptome of the 96 h developmental program. This data revealed that many genes were sequentially expressed in discrete modules, with expression peaking during aggregation, in the transition from aggregation to sporulation, or during sporulation. Analysis of genes expressed at each specific time point provided a global framework integrating regulatory factors coordinating motility and differentiation in the developmental program. These data provided insights as to how starving cells obtain energy and precursors necessary for assembly of fruiting bodies and into developmental production of secondary metabolites. This study offers the first global view of developmental transcriptional profiles and provides an important scaffold for future studies.IMPACT STATEMENTInvestigation of global gene expression profiles during formation of theMyxococcus xanthusspecialized biofilm reveals a genetic regulatory network that coordinates cell motility, differentiation, and secondary metabolite production.

scholarly journals Transcriptome dynamics of the Myxococcus xanthus multicellular developmental program

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
José Muñoz-Dorado ◽  
Aurelio Moraleda-Muñoz ◽  
Francisco Javier Marcos-Torres ◽  
Francisco Javier Contreras-Moreno ◽  
Ana Belen Martin-Cuadrado ◽  
...  
Keyword(s):  
Myxococcus Xanthus ◽  
Developmental Cycle ◽  
Fruiting Bodies ◽  
Rna Seq ◽  
Specific Time ◽  
Future Studies ◽  
Developmental Program ◽  
Global View ◽  
Specific Time Point ◽  
Time Point

The bacterium Myxococcus xanthus exhibits a complex multicellular life cycle. In the presence of nutrients, cells prey cooperatively. Upon starvation, they enter a developmental cycle wherein cells aggregate to produce macroscopic fruiting bodies filled with resistant myxospores. We used RNA-Seq technology to examine the transcriptome of the 96 hr developmental program. These data revealed that 1415 genes were sequentially expressed in 10 discrete modules, with expression peaking during aggregation, in the transition from aggregation to sporulation, or during sporulation. Analysis of genes expressed at each specific time point provided insights as to how starving cells obtain energy and precursors necessary for assembly of fruiting bodies and into developmental production of secondary metabolites. This study offers the first global view of developmental transcriptional profiles and provides important tools and resources for future studies.

Effect of ovarian hormones on the healthy equine uterus: a global gene expression analysis

2016 ◽  
Vol 28 (11) ◽  
pp. 1810 ◽  
Author(s):  
Christina D. Marth ◽  
Neil D. Young ◽  
Lisa Y. Glenton ◽  
Drew M. Noden ◽  
Glenn F. Browning ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Cell Communication ◽  
Gene Expression Profiles ◽  
Global Gene Expression ◽  
Rna Seq ◽  
Complete Understanding ◽  
Pathway Enrichment ◽  
Uterine Inflammation ◽  
Global Gene Expression Analysis

The physiological changes associated with the varying hormonal environment throughout the oestrous cycle are linked to the different functions the uterus needs to fulfil. The aim of the present study was to generate global gene expression profiles for the equine uterus during oestrus and Day 5 of dioestrus. To achieve this, samples were collected from five horses during oestrus (follicle >35 mm in diameter) and dioestrus (5 days after ovulation) and analysed using high-throughput RNA sequencing techniques (RNA-Seq). Differentially expressed genes between the two cycle stages were further investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The expression of 1577 genes was found to be significantly upregulated during oestrus, whereas 1864 genes were expressed at significantly higher levels in dioestrus. Most genes upregulated during oestrus were associated with the extracellular matrix, signal interaction and transduction, cell communication or immune function, whereas genes expressed at higher levels in early dioestrus were most commonly associated with metabolic or transport functions, correlating well with the physiological functions of the uterus. These results allow for a more complete understanding of the hormonal influence on gene expression in the equine uterus by functional analysis of up- and downregulated genes in oestrus and dioestrus, respectively. In addition, a valuable baseline is provided for further research, including analyses of changes associated with uterine inflammation.

scholarly journals Identification of Transcription Factors Involved in the Regulation of Flowering in Adonis Amurensis Through Combined RNA-seq Transcriptomics and iTRAQ Proteomics

Genes ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 305 ◽  
Author(s):  
Zhou ◽  
Sun ◽  
Dai ◽  
Feng ◽  
Zhang ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Low Temperatures ◽  
Molecular Mechanisms ◽  
Developmental Stages ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Global Gene Expression ◽  
Early Spring ◽  
Rna Seq ◽  
Itraq Proteomics

Temperature is one of the most important environmental factors affecting flowering in plants. Adonis amurensis, a perennial herbaceous flower that blooms in early spring in northeast China where the temperature can drop to −15 °C, is an ideal model for studying the molecular mechanisms of flowering at extremely low temperatures. This study first investigated global gene expression profiles at different developmental stages of flowering in A. amurensis by RNA-seq transcriptome and iTRAQ proteomics. Finally, 123 transcription factors (TFs) were detected in both the transcriptome and the proteome. Of these, 66 TFs belonging to 14 families may play a key role in multiple signaling pathways of flowering in A. amurensis. The TFs FAR1, PHD, and B3 may be involved in responses to light and temperature, while SCL, SWI/SNF, ARF, and ERF may be involved in the regulation of hormone balance. SPL may regulate the age pathway. Some members of the TCP, ZFP, MYB, WRKY, and bHLH families may be involved in the transcriptional regulation of flowering genes. The MADS-box TFs are the key regulators of flowering in A. amurensis. Our results provide a direction for understanding the molecular mechanisms of flowering in A. amurensis at low temperatures.

scholarly journals Granulosa cells from human primordial and primary follicles show differential global gene expression profiles

2018 ◽  
Vol 33 (4) ◽  
pp. 666-679 ◽  
Author(s):  
E H Ernst ◽  
S Franks ◽  
K Hardy ◽  
P Villesen ◽  
K Lykke-Hartmann
Keyword(s):  
Gene Expression ◽  
Granulosa Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Global Gene Expression

scholarly journals Dynamical consequences of regional heterogeneity in the brain’s transcriptional landscape

2020 ◽  
Author(s):  
Gustavo Deco ◽  
Kevin Aquino ◽  
Aurina Arnatkevičiūtė ◽  
Stuart Oldham ◽  
Kristina Sabaroedin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Global Gene Expression ◽  
Brain Regions ◽  
Biophysical Model ◽  
Neuronal Dynamics ◽  
Regional Heterogeneity ◽  
Magnetic Resonance Imaging Mri

AbstractBrain regions vary in their molecular and cellular composition, but how this heterogeneity shapes neuronal dynamics is unclear. Here, we investigate the dynamical consequences of regional heterogeneity using a biophysical model of whole-brain functional magnetic resonance imaging (MRI) dynamics in humans. We show that models in which transcriptional variations in excitatory and inhibitory receptor (E:I) gene expression constrain regional heterogeneity more accurately reproduce the spatiotemporal structure of empirical functional connectivity estimates than do models constrained by global gene expression profiles and MRI-derived estimates of myeloarchitecture. We further show that regional heterogeneity is essential for yielding both ignition-like dynamics, which are thought to support conscious processing, and a wide variance of regional activity timescales, which supports a broad dynamical range. We thus identify a key role for E:I heterogeneity in generating complex neuronal dynamics and demonstrate the viability of using transcriptional data to constrain models of large-scale brain function.

scholarly journals A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matching

2021 ◽  
Author(s):  
Taguchi Y-h. ◽  
Turki Turki
Keyword(s):  
Gene Expression ◽  
Learning Strategies ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Tensor Decomposition ◽  
Integrated Analysis ◽  
Rna Seq ◽  
Prior Learning ◽  
Multiple Gene ◽  
Machine Learning Applications

Abstract The integrated analysis of multiple gene expression profiles measured in distinct studies is always problematic. Especially, missing sample matching and missing common labeling between distinct studies prevent the integration of multiple studies in fully data-driven and unsupervised manner. In this study, we propose a strategy enabling the integration of multiple gene expression profiles among multiple independent studies without either labeling or sample matching, using tensor decomposition-based unsupervised feature extraction. As an example, we applied this strategy to Alzheimer’s disease (AD)-related gene expression profiles that lack exact correspondence among samples as well as AD single-cell RNA-seq (scRNA-seq) data. We found that we could select biologically reasonable genes with integrated analysis. Overall, integrated gene expression profiles can function analogously to prior learning and/or transfer learning strategies in other machine learning applications. For scRNA-seq, the proposed approach was able to drastically reduce the required computational memory.

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